AI Solutions Architect hiring kit

Every step to hire the architect who decides how AI fits the systems you already run — and says plainly what the design can't promise.

About this kit — the 5Ws

An AI solutions architect decides how an AI capability should fit into systems that already exist, whether those belong to a customer or to your own organisation. They weigh building against buying or fine-tuning, sketch the reference architecture, work out what data the system may touch and who must approve that, set budgets for running cost and response time, and plan what happens when the model is wrong or unavailable. The job is design and advice rather than hands-on integration: a strong architect writes a proposal engineers can build from and executives can say yes or no to, and is clear about what the design cannot deliver.

Calibrated for:: Calibrated for experienced engineers, architects or technical consultants who have designed systems that other people then built and ran — judgement about budgets, data access and failure modes comes from having watched designs meet reality.
Timeline:: Set an approval-to-offer target at intake, allowing for panel availability, candidate adjustments, references and any lawful checks.

Who: Engineering leaders, heads of solutions and recruiters hiring someone to design how AI features fit into existing systems — for customers before and after a sale, or for their own organisation's platforms.
What: Use this kit to define what good design evidence looks like, test it with a written design review, run structured technical interviews on build-or-buy choices, data access and budgets, and measure how the hiring process performed once someone accepts.
When: Use it when requests for AI features are arriving faster than anyone can judge whether they fit, what they will cost and what could go wrong — from approving the role through reviewing how the search itself ran.
Where: Technology companies selling AI capabilities to business customers, and organisations designing AI features into their own platforms.
Why: A confident architecture diagram is easy to draw and hard to check in conversation. The written design review, structured questions and shared rubric let you compare how each candidate handled the same constraints, which risks they ranked highest and what they chose to leave out of their promises.

The competency model · 6

Use these competencies to compare job-related evidence. Review any apparent gap in a must-have before deciding.

Solution and integration architectureMust-have · 20
Produces a design that works with the systems that actually exist — their interfaces, update schedules and owners — and shows where the model sits, what it reads, what it writes and what it hands back.
Build, buy or fine-tune judgementMust-have · 20
Chooses between building on a general model, buying a ready-made product or service, and fine-tuning a model on the organisation's own examples by looking at volume, available data, upkeep and lock-in, and writes down why.
Data access and security reviewMust-have · 15
Establishes which data a design truly needs, where it may be processed and stored, who can see it and for how long, then takes the design through security and privacy review and adapts it when the answer is no.
Failure modes and fallback designMust-have · 15
Names how the design can fail — wrong output, slow output, a provider outage, stale source data — and defines what the system and its users should experience in each case, including the quiet failures nobody would spot unprompted.
Cost and latency budgetingCan learn · 15
Estimates running cost and response time before anything is built, shows the working and the assumptions, allows for peaks and retries, and later checks the estimate against what the built system actually does.
Trade-off communication for engineers and executivesMust-have · 15
Explains the same design in technical depth to engineers and in terms of outcome, cost, risk and time to executives, and states plainly what the design will not do.

What's required and what's optional

Your hiring journey for AI Solutions Architect. Select a stage to jump to it.

Required for every hire
Optional — use when it adds evidence
AI boost — a big drafting win
Colour = phase

Phase 1
Plan
1. 01Plan the roleRequired
2. 02Agree successRequiredAI boost
Phase 2
Attract
1. 03Write the jobRequiredAI boost
2. 04Find candidatesOptionalAI boost
Phase 3
Select
Phase 4
Decide
Phase 5
Onboard & learn
1. 12Help them settle inRequired
2. 13Measure time to hireRequiredAI boost

AI drafts, people decide: Use AI to draft, rewrite and summarise your own material — never to rank, score, screen out or decide on candidates, and never with a candidate's personal data.

Plan

Attract

Select

Decide

Onboard & learn

Solid = required · dashed = optional · arrow keys move between steps

Stage 1 of 13Required

0 of 3 done

Plan the role

Workforce planning and requisition approval

Who runs it: Hiring manager + finance / HR business partner
Done when: An approved requisition with a written business case, budget and a named decision-maker.

Key tool

Answer these before the role is approved

1Where are decisions about how AI features fit existing systems being made today, and by whom?
2Which engagements or internal projects stalled or had to be reworked last year because the design did not fit the systems, the budget or the data rules?
3Will this person face customers, internal teams or both — and how would their week divide between them?

AI as an advantage4D framework

Delegate: Hand AI the first draft of the business case and the build / borrow / buy comparison. The decision, the budget and the sign-off stay with people.

Show the prompt and the checks

Describe: You are helping a hiring manager write a business case for an AI Solutions Architect role. Context: I will paste the problem the hire solves, the work it involves, the capacity we already have and the time we have. Produce: (1) a one-sentence problem statement in business terms; (2) a build / borrow / buy comparison with the strongest argument for each; (3) the questions a finance partner would ask; and (4) a short recommendation with the evidence it rests on. Use British English and plain language. Do not invent figures — leave [brackets] where I need to supply numbers. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check every number and claim against your own notes and strike anything the draft assumed. Make sure 'buy' is argued from ongoing work, not from a job title.
Diligence: The hiring manager owns the final case and the budget request. No candidate information belongs here; AI drafts the case, people decide whether to open the role.

Don't miss: Hiring an architect when what you need is someone to wire the integration in by hand inside the customer's environment. That is a forward deployed engineer, and the two roles are judged on different results.

Framework

Build · Borrow · Buy

Before opening a role, decide whether to build the capability in your current team, borrow it (contractor, agency, partner), or buy it (a permanent hire).

Write the problem the hire solves in one sentence, in business terms — not the job title.
Test Build: could an existing person grow into this within the time you have?
Test Borrow: is the need time-boxed, or does it end once a system is shipped?
Choose Buy only when the work is ongoing, core to the product and nobody inside can absorb it.
Record the decision, the budget and the one person who signs off the final hire.

Why it works: Writing the build, borrow or buy decision down exposes a vague or temporary need before it becomes a permanent requisition — and gives every later stage an anchor to check against.

• Name the final decision-maker: usually the head of solutions or the engineering leader whose teams build from this person's designs.

Is this a dedicated role?

Decide whether the work is sustained and substantial enough for a dedicated owner by writing down the workload, the risk, the capacity you already have and the outcomes you expect.

Answer these before the role is approved

1.Where are decisions about how AI features fit existing systems being made today, and by whom?
Listen for: A named gap: designs agreed on sales calls without an engineer present, or settled by whichever team gets there first.
Red flag: 'Our engineers already handle it', with no sign that anyone owns the design.
2.Which engagements or internal projects stalled or had to be reworked last year because the design did not fit the systems, the budget or the data rules?
Listen for: Specific projects and a cause traced to the design rather than to the model's quality.
3.Will this person face customers, internal teams or both — and how would their week divide between them?
Listen for: A clear answer, because it decides who sits on the panel and what the work sample looks like.
4.Could a senior engineer already on the team take on design reviews with some support?
Listen for: A named person and a realistic path, or a clear reason why nobody fits.
5.What happens to deals or projects in the meantime if nobody fills this role this year?
Listen for: A concrete consequence: pilots that never become contracts, rework after security review, spend nobody estimated.

Build, borrow or buy?

Build: give a senior engineer who already talks to customers protected time and a mentor so they can grow into design reviews.
Borrow: engage an independent architect for one high-stakes engagement, on condition that a written design record stays with the team afterwards.
Buy: hire a permanent AI solutions architect once design requests across customers or teams outrun what any one engineer can review alongside their own work.

Watch out for

Treating the role as senior sales support whose output is slides. If nobody builds from the designs, the hire has nothing to be measured against.

Stage 2 of 13Required

0 of 2 done

Agree success

Intake meeting and competency model

Who runs it: Recruiter + hiring manager
Done when: A signed-off competency model: 5–7 competencies, each marked must-have or trainable, with weights.

Key tool

Ask the hiring manager

1Which of the systems or areas this person will own worries you most, and why?
2When something there went wrong recently, how did you find out?
3In 12 months, what will be shipped, fixed or running for you to call this hire a success? Agree two or three measurable outcomes.
4Who will they work with most closely?
5What could a strong candidate learn in their first three months? Those capabilities are 'trainable' — never use them to filter applications.

AI as an advantageAI boost4D framework

Delegate: Hand AI your raw intake notes to turn into a draft competency model. Choosing the must-haves and the weights stays with the hiring manager and recruiter.

Show the prompt and the checks

Describe: You are helping a recruiter and a hiring manager build a competency model for an AI Solutions Architect role. Context: I will paste the notes from our intake meeting, including the 12-month outcomes and what the hiring manager worries about most. Produce a table of 5–7 observable competencies with, for each: a one-line definition; whether it is a must-have or trainable, with the reason; what evidence of it looks like in real work; and a suggested weight so the weights total 100. Use British English and inclusive wording, avoid years-of-experience or named employers as evidence, and keep every competency job-related. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that each competency is observable and job-related, that nothing describes a type of person rather than the work, and that trainable items haven't been marked must-have.
Diligence: The hiring manager signs off the final model and weights before anyone meets a candidate. No candidate data goes into the prompt; AI drafts, people decide.

Don't miss: Making a cloud provider's architecture certification a must-have. It shows study, not whether the person can design around a customer's decades-old system.

Framework

Competency model (must-have vs trainable)

A short list of the observable capabilities the job needs, split into what the hire must bring on day one and what you will teach.

Start from the 12-month outcomes, then ask which capabilities produce them.
Keep 5–7 competencies. With more, interviewers struggle to score each one separately.
Mark each must-have or trainable. Only must-haves can count against a candidate.
Give each a weight so they sum to 100, agreed before anyone meets a candidate.
Write what evidence of each looks like, so interviewers know what they are listening for.

Why it works: Agreeing the criteria before meeting candidates stops the bar moving from one candidate to the next, and makes it harder for 'I'll know it when I see it' to decide the hire.

Core intake questions (ask for every role)

Book 45 minutes with the hiring manager before anything is advertised, and write the answers down.
Which of the systems or areas this person will own worries you most, and why?
When something there went wrong recently, how did you find out?
In 12 months, what will be shipped, fixed or running for you to call this hire a success? Agree two or three measurable outcomes.
Who will they work with most closely?
What could a strong candidate learn in their first three months? Those capabilities are 'trainable' — never use them to filter applications.
Who is on the interview panel, and is their time held for the next four to six weeks?

Questions specific to this role

1.Who will build from this person's designs — your engineers, the customer's engineers, a partner — and how much say will the architect have over them?
Listen for: Whether the role advises or decides. It changes how much weight communication carries against technical depth.
2.Which data rules come up most in your engagements: where data may be stored, who may see it, how long it is kept?
Listen for: The constraints that should appear in the work sample's constraint sheet, and how much weight data access and security review deserves.
3.What does a finished design look like here today — a slide, a written document, a diagram, or nothing at all?
Listen for: The starting point. If nothing is written down now, the first months will go on setting a design format as well as using it.

What success looks like in 12 months

Every significant AI engagement or project has a written design that states its data access, cost budget, latency target and fallback.
At least one build, buy or fine-tune decision is recorded with its reasons, so the next team does not reopen it from scratch.
Engineers say designs reach them with fewer open questions about data access and limits.
Executives can repeat back, in plain words, what a proposal will and will not deliver before they approve it.

Watch out for

Expecting the architect to also own delivery of every design without agreeing where their responsibility ends and the build team's begins.

Stage 3 of 13Required

0 of 3 done

Write the job

Job description and pay band

Who runs it: Hiring manager (content) + recruiter (publishing)
Done when: A published job description that states outcomes, the must-haves, how AI tools are used and the pay information the law requires.

Key tool

AI Solutions Architect

We're looking for an AI solutions architect to design how AI capabilities fit into the systems [our customers / our teams] already run. You'll decide when to build, buy or fine-tune, set out the reference architecture, work through data access and security review, put numbers on running cost and response time, and plan for when the model is wrong or unavailable — then explain the trade-offs to engineers and executives in terms each can act on. You'll work closely with [engineering / solutions / product].

What you'll do

•Turn a customer's or team's request into a written design: the systems involved, where the model sits, what it reads and writes, and what it hands back.
•Recommend whether to build on a general model, buy a ready-made product or service, or fine-tune a model — and record the reasons.
•Work out what data a design needs, where that data may go and who must approve it, then take the design through security and privacy review.
•Set cost and latency budgets before anything is built, and check designs against them.
•Name the ways a design can fail — wrong answers, slow responses, a provider outage — and design the fallback for each.
•Explain trade-offs to engineers in technical detail and to executives in terms of outcome, risk, cost and time.
•Maintain reference architectures and design decision records that other teams can reuse.

What you need

•You have designed at least one system that connected a model or other AI capability to existing business systems, and seen it built and running.
•You can show a written design — a proposal, decision record or review — that other engineers built from.
•You have taken a design through a data-access, security or privacy review and changed it as a result.
•You can estimate the running cost and response time of a design and explain how you reached the numbers.

Nice to have

•Experience on the customer side of an engagement, before or after a sale.
•Hands-on engineering in [Python / TypeScript / Java] recent enough to judge what a design will take to build.
•Experience comparing hosted model providers with self-hosted models on cost, latency and data handling.
•Awareness of AI-specific rules that affect your customers' sectors, such as the EU AI Act.

How we use AI tools

•Where company policy permits, use approved AI tools to draft designs, comparisons and estimates, and check each figure and claim before it reaches a customer or an executive.
•Recognise when a request does not need a model at all, and say so in the design.
•For the work sample, use any AI tools you would use at work and note which ones you used and for what.

Pay

Salary range: [currency, minimum–maximum], plus [equity/bonus/benefits]. The range reflects [location/working pattern]. We place every offer within it using the same pre-set, job-related, gender-neutral factors — level, whether the role is customer-facing, and how much travel it carries, breadth of systems and sectors the person has designed for — never previous salary or negotiating style.

AI as an advantageAI boost4D framework

Delegate: Hand AI the first draft of the job description from your signed-off competency model. The must-haves, the pay range and the final wording stay with people.

Show the prompt and the checks

Describe: You are helping write an outcome-based job description for an AI Solutions Architect role. Context: I will paste the competency model (must-have and trainable, with evidence), the 12-month outcomes, how the team uses AI tools, the location and working pattern, and the pay range. Produce: a summary paragraph that leads with the problem and what the hire will own; 'What you'll do' as outcomes, not tasks; 'What you need' containing only the must-haves; 'Nice to have' for the trainable items; 'How we use AI tools'; and a pay section that states the range. Use British English and inclusive, gender-neutral wording, avoid hype, and replace any 'X years' with the capability it stands for. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that 'What you need' contains only the must-haves, that no degree, employer or years requirement crept in, and that the pay range and location are exactly what was approved.
Diligence: The hiring manager owns the content and the recruiter owns publishing, including the pay information the law requires for each location. AI drafts the advert; people decide what it says.

Don't miss: Listing every model provider and vector database by name. The list dates quickly and filters for keyword matches rather than design judgement.

Stage 4 of 13Optional

0 of 3 done

Find candidates

Sourcing strategy

Who runs it: Recruiter / sourcer
Done when: A shortlist-ready pipeline from at least three channels, with outreach tracked.

Use when:: Inbound applications won't give you enough qualified candidates.
Skip when:: Your shortlist already comes from inbound applications or referrals.

Key tool

Search query

("solutions architect" OR "AI architect" OR "principal engineer" OR "technical architect") AND ("LLM" OR "generative AI" OR "machine learning") AND ("reference architecture" OR "integration" OR "design review" OR "build vs buy") AND ("latency" OR "cost" OR "security review" OR "data residency") NOT ("intern" OR "student")

First outreach message

AI as an advantageAI boost4D framework

Delegate: Hand AI the search queries and the first draft of the outreach template. Choosing channels, personalising to a candidate's public work and sending stay with the recruiter.

Show the prompt and the checks

Describe: You are helping a recruiter source candidates for an AI Solutions Architect role. Context: I will paste the must-have competencies, the kind of work we want evidence of, the channels we are using and the pay range. Produce: (1) one search query per channel, using OR for alternatives, AND for things that must all appear and NOT to exclude, with a one-line note on what each query looks for; and (2) a first-outreach template of under 120 words that opens with a [placeholder for something the candidate built], states the problem we are hiring to solve and the pay range, and ends with a low-pressure question. Use British English and inclusive wording. Do not refer to any specific person. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the queries search for evidence of the work rather than job titles alone, and that the template claims nothing about the role or the pay you can't back up.
Diligence: The recruiter personalises every message to something the candidate actually built, and sends it. No names or profile data go into the prompt; AI drafts, people decide whom to contact.

Don't miss: Searching only for the solutions-architect title. Strong candidates often sit under staff engineer, principal engineer or technical consultant titles.

Framework

Channel mix + evidence-first outreach

Spread sourcing across channels where the work is visible, and open outreach with the problem, not the perks.

Pick channels where candidates show their work, not only where they list job titles.
Write one search query per channel. OR joins alternatives, AND joins things that must all appear, NOT excludes. Keep the version that works best.
Personalise the first line to something the candidate actually built or wrote.
Lead with the problem and the pay range. Save the perks for later.
Track reply rates by channel each week and drop the channel that isn't working.

Why it works: Searching for evidence of the work can surface candidates whose current title differs from the vacancy, or whose CV undersells what they have done.

Sourcing basics

Adapt the search query for each platform — the same words behave differently on each site.
Candidates with adjacent titles may have relevant evidence. Search for the work and outcomes the role needs, not the current title.
Channel activity is a lead, not evidence. Review linked work and examples against the written competencies.

Where to look

Architecture write-ups, design documents and decision records the candidate has published

They show how the person reasons about constraints in writing, which is the main output of this role.

Conference talks on fitting AI into existing systems

A talk about a real integration shows which trade-offs the speaker chose to explain and which they skipped over.

Referrals from engineers who built from the candidate's designs

Someone who built from a design can tell you whether it held up once the work started.

LinkedIn and similar networks, searched for design and integration language rather than the job title

The same work shows up under staff engineer, principal engineer and consultant titles.

Consultancy and technology-partner networks

People who have advised several organisations can compare many systems; check for depth in at least one of them.

Boolean search string

("solutions architect" OR "AI architect" OR "principal engineer" OR "technical architect") AND ("LLM" OR "generative AI" OR "machine learning") AND ("reference architecture" OR "integration" OR "design review" OR "build vs buy") AND ("latency" OR "cost" OR "security review" OR "data residency") NOT ("intern" OR "student")

First outreach message

Hi [name] — your [talk / write-up] on [specific design decision] stood out, especially [detail]. At [company], customers keep asking how AI features can sit alongside systems they can't replace, under budgets and data rules that differ for each of them. We're hiring someone to design those answers and explain the trade-offs, paying [range]. Would you be open to a 20-minute call about what that work looks like here?

Stage 5 of 13Required

0 of 3 done

Check applications

Application screening

Who runs it: Recruiter
Done when: Every application reviewed against the same written knockouts within five working days.

Key tool

Reject only if one of these is missing

•Can meet the role's work-authorisation requirements by the start date, with sponsorship where the employer offers it. Don't ask for documents at this stage.
•Describes at least one design that connected an AI capability to existing systems and was then built — not only a proposal or a demo.
•Has produced written design work that others built from, such as a design document, decision record or architecture proposal.

AI as an advantage4D framework

Delegate: Hand AI the job of turning your written knockouts into a consistent checklist with reason codes. Reviewing each application stays with the recruiter.

Show the prompt and the checks

Describe: You are helping a recruiter prepare an application-screening checklist for an AI Solutions Architect role. Context: I will paste the must-have competencies and our written knockout criteria. Produce: (1) each knockout as a yes/no question answerable from an application alone; (2) a short list of reason codes for a 'no'; and (3) a note of any criterion that looks like a proxy — a degree, employer brand or employment gap — rather than a job-related requirement, with a suggested rewrite. Use British English. Do not evaluate any applicant and do not ask for applicant details. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that each question is binary and job-related, and that every flagged proxy has been removed or justified in writing.
Diligence: A person reviews every application against the checklist and records the reason. Never paste CVs or applicant details into an AI tool, and never let a tool screen anyone out — in New York City and the EU that can bring automated-decision rules into play.

Don't miss: Counting certifications, or the number of cloud services listed, as evidence of design ability.

Framework

Knockout criteria

A small set of yes/no requirements, written in advance, applied to every applicant the same way.

Derive knockouts only from must-have competencies and genuine legal requirements.
Keep them binary and answerable from the application itself.
Apply them in the same order to every application and log the reason for each rejection.
Do not use proxies such as university, employer brand or employment gaps as knockouts.
Anything ambiguous gets a second review or moves forward to the skills stage.

Why it works: Early screening rejects people before richer evidence exists, so its criteria need the most care. Written criteria make decisions reviewable — but each one must also be job-related, applied consistently and checked for unjustified adverse impact.

Screening rules

Do not use degrees, employment gaps or certifications as filters unless they are demonstrably job-related and justified for this role.
Judge the claims in a CV, not how polished it is — a well-written CV may or may not have had AI help, and either way it is the evidence that matters.

Knockout criteria

Can meet the role's work-authorisation requirements by the start date, with sponsorship where the employer offers it. Don't ask for documents at this stage.
Describes at least one design that connected an AI capability to existing systems and was then built — not only a proposal or a demo.
Has produced written design work that others built from, such as a design document, decision record or architecture proposal.

Green flags

Names a constraint — a budget, a data rule, a legacy system — and explains how it changed the design.
Mentions an option they recommended against, and why.
Links to a write-up, talk or design document you can read.

Red flags

Lists cloud services and certifications but cannot name one system they designed around.
Presents every engagement as a success, with no constraint, trade-off or change of plan along the way.
Talks about 'driving AI strategy' without a single design that went on to be built.

Watch out for

Marking a candidate down because their past designs ran on a different cloud or model provider from yours.

Compliance checkpointNew York CityEU

For a New York City employment decision, work out whether a tool is an 'automated employment decision tool' (AEDT) under Local Law 144 — one that uses covered techniques to substantially assist or replace a discretionary decision. Before using a covered tool, make sure an independent bias audit was completed no more than a year earlier, publish the required summary, and give covered candidates or employees the required notice at least ten business days before use. In the EU, the AI Act (Annex III) lists AI used to recruit, filter applications or evaluate candidates as high-risk, subject to the Article 6(3) exception; the related obligations apply from 2 December 2027 under the amended timetable. A manual screen does not exempt an AI tool used elsewhere in the process.

Sources: NYC DCWP — Automated employment decision tools · EU AI Act — consolidated Regulation (EU) 2024/1689 · checked 2026-09-25

Stage 6 of 13Optional

0 of 3 done

Recruiter call

Recruiter screen

Who runs it: Recruiter
Done when: A 20–30 minute call that confirms logistics, motivation and one or two must-have signals.

Use when:: You are hiring at volume, or pay and logistics need confirming early.
Skip when:: You have only a handful of applicants — fold the pay and logistics questions into application screening instead.

Key tool

Ask every candidate, in this order

1Tell me about a recent design you were responsible for. Which existing systems did it have to work with?
2Tell me about a time you recommended against building something, or against a particular approach. What happened next?
3Who did you have to persuade on that design, and how did you explain the trade-off to them?
4Our range for this role is [range]. Does that work for you?

AI as an advantage4D framework

Delegate: Hand AI the first draft of the screening-call script and its evidence anchors. Running the call and taking the notes stay with the recruiter.

Show the prompt and the checks

Describe: You are helping a recruiter write a 20–30 minute structured screening call for an AI Solutions Architect role. Context: I will paste the must-have competencies, the logistics we need to confirm (location, working pattern, notice period) and the pay range. Produce: an opening that explains the process, the timeline and the pay range; 4–6 questions in a fixed order, each with one line on what a strong answer contains; and a closing that asks whether the published range works. Use British English and inclusive wording. Do not include questions about salary history, age, family, health or any other protected characteristic. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that every question is job-related and lawful, that the pay question asks whether the range works rather than for history, and that the anchors describe evidence, not impressions.
Diligence: The recruiter asks the same questions of everyone and writes the notes. No candidate details go into the prompt; AI drafts the script, people run the call and decide.

Don't miss: Asking the recruiter to grade architecture answers. Their part is motivation, availability and whether the candidate separates their own decisions from the team's; design quality is for the panel.

Framework

Structured screening call

The same short set of questions for every candidate, with notes taken against what a strong answer contains.

Open by explaining the process, timeline and pay range. No surprises later.
Confirm logistics: location, working pattern, notice period and whether the range works for them.
Ask the same 4–6 questions in the same order, and note evidence, not impressions.
Leave time for the candidate's questions. Don't score their questions unless they test a written, job-related competency.
Decide within 48 hours and tell the candidate either way.

Why it works: Using the same questions and written evidence anchors makes answers easier to compare, and a short, predictable call respects the candidate's time.

Screening call basics

Set the decision timetable before sourcing and tell candidates when to expect each update.
Ask every candidate what draws them to this particular role, and listen for interest in the work described rather than the title.
Confirm notice period and whether they are in other processes, so you can plan the timetable.
End every call by asking whether the published pay range works — never ask for salary history.

Recruiter screen questions

1.Tell me about a recent design you were responsible for. Which existing systems did it have to work with?
Listen for: Named systems, a constraint, and decisions the candidate made personally.
Red flag: Describes only the model or the vendor, never the systems around it.
2.Tell me about a time you recommended against building something, or against a particular approach. What happened next?
Listen for: A reasoned recommendation, the evidence behind it, and how it was received.
Red flag: Cannot recall ever advising against anything, or did so without giving reasons.
3.Who did you have to persuade on that design, and how did you explain the trade-off to them?
Listen for: A specific audience and an explanation adjusted to what that audience cared about.
Red flag: 'Everyone agreed straight away', with no sign a real decision was made.
4.Our range for this role is [range]. Does that work for you?
Listen for: A clear answer on whether the published range works. Don't ask for salary history — this process sets pay from the published band and pre-set, job-related factors.

Compliance checkpointUS (state and city)EU

Don't ask for salary history. In the EU, national laws implementing Article 5(2) of Directive (EU) 2023/970 must prohibit the question. US rules vary, but jurisdictions including California and New York prohibit employers from seeking salary history. Ask whether the published range works instead.

Sources: EU Pay Transparency Directive 2023/970, Article 5 · California Labor Commissioner — salary history · New York State DOL — salary history ban · checked 2026-09-25

Stage 7 of 13Required

0 of 3 done

Work sample

Skills assessment / work sample

Who runs it: Hiring manager (design) + recruiter (logistics)
Done when: Every shortlisted candidate completes the same job-shaped task, marked against criteria written in advance.

Key tool

Write a design review for a broker-email assistant

A fictional mid-sized commercial insurer wants an assistant that reads incoming broker emails, summarises each one and suggests which policy it relates to, so claims and underwriting handlers see the summary in their case queue. Their current systems: an email gateway that receives all broker mail; a case-handling tool whose supported API can create a case and attach a note to it; a policy administration system with no API, which only produces a nightly export file of policies; a document store holding email attachments; and a single sign-on service that controls staff access. Emails and attachments contain policyholders' names, addresses and claim details. The constraints: around 6,000 emails a week with a Monday-morning peak, a summary visible to the handler within ten seconds of the email arriving, a fixed monthly running budget of 4,000 euros, policy and personal data that must stay in the EU, and any new external processor of personal data needing approval from the insurer's security and data-protection teams. A diagram of these systems comes with the brief for convenience; everything you need is in this text. Write a one-to-two page architecture proposal addressed to the insurer's head of operations and their lead engineer. You do not need to write code or price exact services: state your assumptions and show how you reached your estimates.

Planned for up to three hours (adjustments available on request). At the agreed stopping time, write a short note on what you would examine next, and what you deliberately left out.

AI as an advantage4D framework

Delegate: Hand AI the first draft of the work-sample brief and its marking sheet. Choosing the task, setting the timebox and marking stay with the hiring manager.

Show the prompt and the checks

Describe: You are helping a hiring manager design a work sample for an AI Solutions Architect role. Context: I will paste the competency model, a real problem the team solved recently with anything confidential removed, the timebox, and our AI-tool policy for candidates. Produce: a candidate-facing brief (what to do, what to hand in, the time cap, how to request an adjustment, which AI tools are allowed and how to report their use); and a marking sheet with 4–6 criteria tied to named competencies, each with what a 1, a 3 and a 5 look like. Use British English and plain, inclusive wording. Do not design anything that needs more than the stated time. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the task fits the timebox, that every criterion maps to a competency, and that the brief gives all candidates the same starting materials.
Diligence: The hiring manager owns the task and the criteria, and people mark every submission. Do not paste candidate submissions into an AI tool for scoring; AI drafts the brief, people mark and decide.

H-EvaluateH-Evaluate can generate a job-specific assessment from your job description, so you don't have to build the task by hand.How H-Evaluate helps →

Don't miss: Asking for a full architecture of a real customer's estate. It turns the work sample into unpaid consulting and rewards whoever has the most free time.

Framework

Work-sample test

A small, realistic slice of the job, marked against criteria written in advance. Give candidates equivalent materials and arrange reasonable adjustments where required.

Take a real problem the team solved recently and shrink it to fit the timebox.
Write the marking criteria first, tied to the competency model.
Timebox honestly — for take-home work, a few hours at most — and say so in the brief.
State the AI-tool policy in writing. Where the job permits these tools, use equivalent conditions in the assessment and evaluate how candidates verify their output.
Mark without CV details where you can, and have two people mark borderline submissions.

Why it works: A well-designed work sample gives direct evidence of job-relevant skill rather than a proxy for it, and gives candidates a realistic preview of the work.

Assessment rules for every candidate

State the planned time cap and how to request an adjustment. At the agreed stopping time, ask candidates to note what they would examine next and what they left out — never reward unpaid overtime.
Give every candidate the same starting materials, and use a second marker for borderline submissions.
Say in writing which AI tools are allowed and ask candidates to tell you how they used them.
Invite every candidate to ask for adjustments, and answer each request promptly.
Pay for anything the task costs the candidate to run, such as model or cloud usage.

The work sample

Write a design review for a broker-email assistant

Timebox: Planned for up to three hours (adjustments available on request). At the agreed stopping time, write a short note on what you would examine next, and what you deliberately left out.

What the candidate hands in

The proposal: the design in words plus one diagram (a photo of a hand drawing is fine), including whether you would build, buy or fine-tune, and why.
Your cost and latency estimate against the constraints, with the assumptions behind it.
A short data-flow and approval plan: which data is sent where, where it is processed and stored, who can access it, and which steps need sign-off from the insurer's security and data-protection teams.
The three biggest risks, ranked, each with what you would do about it.
What you would test first, before anything else is built, and which result would change your design.
What you would decline to promise the customer, written the way you would say it to them.

Marking criteria (write these first)

The design works with the systems in the diagram, including the policy system's nightly export, rather than assuming a live connection that does not exist.
The build, buy or fine-tune choice is justified against this customer's volume, budget and data rules, not general preference.
Cost and latency estimates show their working and are checked against the constraints, including the Monday peak.
The data-flow plan sends only the fields the design needs, keeps personal and policy data in the EU, limits who can read it and its logs, and names each step that needs security or data-protection approval.
The risks are specific to this design — for example, an email matched to a policy that changed after last night's export — rather than generic AI risks.
The first test would retire the riskiest assumption, and the candidate states which result would change the plan.
What the candidate declines to promise is honest and worded so the head of operations could act on it.

Adjustments: Candidates can ask for more time, or talk the proposal through live with an interviewer instead of writing it. No model access or code is needed: the brief, diagram and constraint sheet are self-contained.

Watch out for

Scoring a proposal down for declining to promise something. Saying what a design cannot guarantee is part of the job being tested.

Compliance checkpointUSGreat BritainEU

In the US, covered employers must provide a reasonable accommodation to a qualified applicant with a disability unless it would cause undue hardship (ADA). In Great Britain, the Equality Act 2010 requires reasonable adjustments in recruitment. In the EU, the duty comes from national equality law. Don't ask for more medical information than the law allows.

Sources: EEOC — prohibited practices · Equality Act 2010 · checked 2026-09-25

Stage 8 of 13Required

0 of 3 done

Structured interviews

Structured interview loop

Who runs it: Interview panel (named in advance)
Done when: Every competency has a lead interviewer and at least one second source of evidence, with the same questions for every candidate.

Key tool

Interview questions by pillar

Free in this kit: Domain questions for every round. The other three pillars and the AI-fluency check are measured by H-Evaluate.

Free in this kit

Senior architect or principal engineer who marked the proposal · Solution and integration architecture · Failure modes and fallback design

1.In your proposal, the policy system only updates overnight. Walk me through how your design handles a broker email about a policy that was changed this morning, and which alternatives you considered.
Solution and integration architecture
Good answerTraces the path the email takes, names the stale-data gap without being prompted, compares at least two options — flagging uncertain matches for the handler, checking the case tool for today's changes, asking the insurer for a more frequent feed — and explains the choice against the constraints.
Weak answerAssumes the policy data is current, or proposes a live connection the diagram does not support without saying what it would cost the insurer to build.
2.Suppose the insurer now wants the summaries written back into the policy system as well as the case tool. What changes in your design, and what would you ask before agreeing?
Solution and integration architecture
Good answerNotes that the policy system has no supported write path, asks who owns that system and what a wrong write would cost, and treats write-back as a separate, higher-risk decision with its own review rather than a small extension.
Weak answerAdds a write-back arrow to the diagram without asking how it would work or who has to approve it.
3.On a Monday morning, the summarisation part of your design — whatever you chose to build, buy or fine-tune — slows down, and summaries start arriving after handlers have already opened the emails. How does your design handle that, and what should a handler see?
Failure modes and fallback design
Good answerLocates the slowdown in their own design (a hosted service, a self-run model, a bought product), sets a cut-off after which the email reaches the queue without a summary, shows the handler the summary is late rather than silently missing, weighs options such as queueing, spare capacity or a second route, and ties the choice to cost and the ten-second target.
Weak answerRetries until the summary arrives, or assumes the component will stay fast, with no defined behaviour for the handler.

Optional follow-ups (1)

4.Which failure in your design would be hardest to notice from outside, and what would you put in place so that someone does notice it?
Failure modes and fallback design
Good answerPicks a quiet failure — a confident summary attached to the wrong policy — and proposes a concrete check, such as handlers reviewing a sample of matched emails or tracking how often handlers change the suggested policy.
Weak answerNames only loud failures such as outages, or says monitoring will catch it without saying what would be measured.

AI as an advantageAI boost4D framework

Delegate: Hand AI the drafting of probe questions and strong / weak anchors for one competency. Asking, listening and scoring stay with the interviewer.

Show the prompt and the checks

Describe: You are helping an interviewer prepare a structured technical interview for an AI Solutions Architect role. Context: I will paste one competency, its evidence description and the core question we ask every candidate. Produce: four probes in this fixed order — what would you do, why that choice, what else did you consider, how would you know it worked — tailored to the question; a one-line description of a strong answer and of a weak answer; and two follow-ups to use only if time allows. Use British English and inclusive wording. Keep everything about the work, nothing about the person. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the probes test the named competency rather than trivia, that the anchors describe observable evidence, and that no question touches a protected characteristic.
Diligence: Interviewers ask the same core questions of every candidate and score alone against the rubric. Interview notes and candidate names never go into an AI tool; AI drafts the questions, people interview and decide.

H-EvaluateH-Evaluate can add evidence on Cognitive, Situational judgement, Behavioural and AI fluency for your panel to review alongside these interviews.How H-Evaluate helps →

Don't miss: Scoring candidates on whether they chose the architecture you would have chosen, instead of whether their reasoning held up against the constraints.

Framework

Structured technical interview

Every candidate gets the same job-specific questions, each interviewer leads on specific competencies, and every answer is probed with the same four follow-ups: what would you do, why, what else did you consider, and how would you know it worked.

Give each competency a lead interviewer, plus a second source of evidence — the work sample, another round or an independent marker. Avoid asking the same thing twice, but never let one person's view be the only evidence for a critical competency.
Ask the three core questions for your round, in order, for every candidate. Use an optional follow-up only if there is time.
Probe each answer: why that choice, what else they considered, and how they would know it worked — until you hear the candidate's own technical decision.
Take notes on evidence during the interview. Don't rate until it's over.
Fill in your interviewer sheet and submit it before you talk to any other interviewer.

Why it works: Structured interviews make answers comparable: every candidate faces the same job-related questions and is scored against the same anchors.

Interview rules

Use the same core questions in the same order for every candidate.
In the work-sample review, understand the candidate's decisions against the rubric — don't turn it into a debate about your preferred solution.
Avoid trivia disconnected from a real job decision. Use a realistic debugging, design or evaluation problem instead.

Watch out for

Letting the conversation turn into a tour of cloud services; bring it back to why each part is there and what it costs.

Compliance checkpointUSUKEU

Don't ask about protected characteristics — such as age, disability, pregnancy or family plans, religion, race or nationality — unless applicable law permits the question for a specific, necessary, job-related purpose, and HR or counsel has confirmed that basis beforehand. Ask only whether the candidate can meet the role's work-authorisation requirements. If a candidate volunteers protected information, don't probe; record only what is lawfully needed, keep it separate and restrict access.

Sources: EEOC — prohibited practices · Equality Act 2010 · checked 2026-09-25

Where to find more interview questions

Use web question banks for inspiration only: ask every candidate the same job-related questions, check each one against this kit's competencies, and never ask about protected characteristics. Don't reuse this kit's work sample from a public bank — candidates can find questions that circulate online, so a widely posted question tests preparation more than skill.

Show 7 sites

US OPM — Structured interviews(opens in a new tab)
US federal guidance on building structured interviews, with a guide, training material and sample questions with rating scales.
FreeFor interviewers
Acas — Interviewing job applicants(opens in a new tab)
UK public guidance on preparing fair interviews, asking applicants the same questions and avoiding discriminatory ones.
FreeFor interviewers
Workable — Interview questions library(opens in a new tab)
Sample interview questions for employers, listed by job role, including machine learning, data and engineering roles.
FreeFor interviewers
Indeed for Employers — Interview questions(opens in a new tab)
Sample interview questions for employers by job title and skill, with notes on what to look for in answers.
FreeFor interviewers
Tech Interview Handbook(opens in a new tab)
Open-source software-engineering interview preparation: coding, system design and behavioural questions.
FreeFor candidates and interviewers
Chip Huyen — Machine Learning Interviews Book(opens in a new tab)
Free online book with knowledge questions on maths, computer science and machine learning, grouped by difficulty.
FreeFor candidates and interviewers
Blind — Interview questions channel(opens in a new tab)
Anonymous posts by tech workers about questions they report being asked. Unverified; some content needs sign-in.
Free, some content needs sign-inCandidate reports, unverified

Links checked 2026-10-05. External sites; we don't control their content.

Stage 9 of 13Required

0 of 2 done

Compare evidence

Scoring, calibration and debrief

Who runs it: Hiring manager (chairs) + panel
Done when: A documented hire / no-hire decision traceable to scored evidence against the competency model.

Key tool

Interactive scorecard

Score one candidate against the rubric. Everything stays in your browser. Download it as a spreadsheet for your records.

Solution and integration architecture Must-have

Weight

What do 1, 3 and 5 look like?

1: Draws a design around the model and assumes the surrounding systems will cooperate.
3: Fits the design to the existing systems once their limits are pointed out.
5: Starts from the systems as they are — interfaces, schedules, owners — and makes each connection's cost and risk visible unprompted.

Build, buy or fine-tune judgement Must-have

Weight

What do 1, 3 and 5 look like?

1: Has a default answer regardless of the customer's data, volume or upkeep.
3: Compares the options sensibly when asked, but without a plan to test the choice.
5: Decides from the customer's own evidence, proposes a small comparison before committing, and records why so the decision can be revisited.

Data access and security review Must-have

Weight

What do 1, 3 and 5 look like?

1: Sends whatever data is convenient and leaves review to someone else.
3: Identifies the sensitive data and the review needed, with prompting on alternatives.
5: Reduces the data to what is needed before review, and arrives with costed alternatives if the answer is no.

Failure modes and fallback design Must-have

Weight

What do 1, 3 and 5 look like?

1: Describes only the path where everything works.
3: Covers outages and slow responses; misses quiet failures such as confident wrong matches.
5: Names loud and quiet failures, defines what users see in each, and proposes a way to detect the quiet ones.

Cost and latency budgeting Can learn

Weight

What do 1, 3 and 5 look like?

1: No estimate, or a figure with no working.
3: A reasonable estimate with assumptions stated; peaks or retries left out.
5: Shows the working, allows for peaks, retries and growth, and plans to check the estimate against the built system.

Trade-off communication for engineers and executives Must-have

Weight

What do 1, 3 and 5 look like?

1: One explanation for every audience, heavy with jargon or vague about limits.
3: Adjusts the explanation for executives when asked; limits are mentioned but buried.
5: Gives each audience what it needs to decide, states limits plainly, and handles disagreement without overruling or giving way.

Weighted score– / 5

AI as an advantage4D framework

Delegate: Hand AI the drafting of a blank debrief worksheet and agenda from the competency rubric alone. People add the evidence locally; the scores and the decision stay with the panel.

Show the prompt and the checks

Describe: You are helping a hiring manager prepare a debrief for an AI Solutions Architect role. Context: I will paste the competency rubric only — the competencies, their weights and their 1, 3 and 5 anchors. Produce: (1) a blank debrief worksheet with one row per competency and empty columns for each interviewer's independent score, the evidence they cite, the spread of scores and an 'evidence gap?' box; (2) a 45-minute agenda: confirm every scorecard is in and reveal all scores at once, discuss the widest spreads using evidence only, review any must-have scored 1, then look at the weighted total and decide (hire, no hire or one targeted follow-up), and record the reasons; and (3) neutral questions the chair can ask to draw out evidence rather than adjectives. Leave every evidence and score cell empty. Do not suggest a score, a rating or a recommendation. Use British English. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the worksheet follows your rubric exactly — same competencies, weights and anchors — and that the agenda keeps independent scores hidden until the reveal.
Diligence: Interviewers fill in the evidence and scores locally, alone, before the debrief, and the panel decides together. Candidate notes and personal data never go into an AI tool; AI never scores, ranks or compares candidates.

H-EvaluateH-Evaluate gives you a report on each candidate's assessment evidence to bring into this debrief.How H-Evaluate helps →

Don't miss: Letting a fluent performance in the hiring-manager round outweigh a weak written proposal.

Stage 10 of 13Optional

0 of 2 done

Check references

References and background checks

Who runs it: Recruiter (references) + HR (checks)
Done when: At least two references that test specific competencies, plus only the checks that are lawful and relevant to the role.

Use when:: It is lawful where you hire, and specific evidence from a referee would change the decision.
Skip when:: Local law or company policy rules it out.

Key tool

Ask each referee

1Tell me about a design [candidate] produced that your team built. What did it get right, and what had to change once building started?
2When [candidate] recommended against an approach, how did they make the case, and did it hold up?
3How did they handle a cost or data question they could not answer on the spot?

AI as an advantage4D framework

Delegate: Hand AI the drafting of reference questions aimed at the competency the panel was least sure about. Choosing referees, making the calls and interpreting the answers stay with the recruiter.

Show the prompt and the checks

Describe: You are helping a recruiter write competency-based reference questions for an AI Solutions Architect role. Context: I will paste the competency model and the one or two competencies where our evidence is thinnest — no candidate details. Produce: 3–4 questions per competency that ask for specific examples ('tell me about a time…') rather than ratings; an opening question about how the referee worked with the person and for how long; and a closing question about what their next manager could do to help them do their best work. Use British English and inclusive wording. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the questions ask for examples rather than opinions, that they stay on job-related competencies, and that none asks about health, family or other protected matters.
Diligence: References are taken with the candidate's consent and interpreted by people. Referee answers and candidate details never go into an AI tool; AI drafts the questions, people decide.

Don't miss: Hearing that the candidate 'presented brilliantly to the board' and treating it as evidence the design itself worked.

Framework

Competency-based referencing

Treat references as a last structured conversation about the candidate's work, aimed at the competencies where your evidence is thinnest.

With the candidate's consent, ask for two people who saw their relevant work directly — ideally a former manager and a peer.
Accept suitable alternatives if a manager or peer isn't available, and never penalise a candidate for a former employer's no-reference policy.
Aim your questions at the one or two competencies the debrief was least sure about.
Ask for examples, not ratings. 'Tell me about a time they…' works here too.
Set the timing of each background check by the law and policy that apply.

Why it works: Generic reference questions tend to produce little job-related evidence. Asking for specific examples tied to the role's competencies turns referencing into something you can use.

Core reference questions and checks

Open with: 'How did you work together, and for how long?' — it tells you how much weight the rest can carry.
Ask: 'Tell me about a time something they owned went wrong. What did they do?'
Close every reference with: 'What should their next manager do to help them do their best work?'
Verify only claims that were material to the decision — don't verify a degree the role never required.
Run criminal-record checks only where the role justifies them and local law allows; many roles don't need one.
Schedule formal work-authorisation checks at the legally required time: before employment starts in the UK; for US Form I-9, Section 1 by the first day of paid work and Section 2 within three business days.

Reference questions specific to this role

1.Tell me about a design [candidate] produced that your team built. What did it get right, and what had to change once building started?
Listen for: A specific design, an honest account of what changed, and whether the changes were foreseeable.
2.When [candidate] recommended against an approach, how did they make the case, and did it hold up?
Listen for: Evidence-based advice that people acted on, rather than opinions that were overruled or ignored.
3.How did they handle a cost or data question they could not answer on the spot?
Listen for: Said what they did not know, followed up with an answer, and did not guess in front of the customer.

Compliance checkpointUS

If a consumer reporting agency provides a background report, the Fair Credit Reporting Act requires a clear, stand-alone written disclosure and the candidate's written authorisation before you order it. Before rejecting someone because of the report, give them a copy and the FCRA summary of rights and a reasonable opportunity to review and dispute it before any adverse action; if you still reject, send a final adverse-action notice. State and local 'fair chance' laws may delay criminal-history questions or checks until after a conditional offer.

Sources: FTC — Background checks: what employers need to know · FTC — Using consumer reports: what employers need to know · checked 2026-09-25

Stage 11 of 13Required

0 of 2 done

Agree the offer

Offer, negotiation and close

Who runs it: Hiring manager + recruiter
Done when: An accepted written offer within the approved band, with a start date.

Key tool

Build the offer around

•The problem: they decide how AI fits into systems that matter, before costly mistakes are built in.
•Authority: designs that teams build from, with a real say in when not to build at all.
•Range: exposure to many customers' or teams' systems, rather than a single product's codebase.
•Progression: what the senior or principal architect level looks like here, written down before they accept.

AI as an advantage4D framework

Delegate: Hand AI the first draft of the offer message built around what the candidate said matters to them. The figure, the approval and the conversation stay with the hiring manager.

Show the prompt and the checks

Describe: You are helping a hiring manager draft an offer message for an AI Solutions Architect role. Context: I will paste three general priority categories (for example: learning, flexibility, scope) — not the candidate's words, notes or personal details; the parts of the offer; and the start-date options — without the salary figure. Produce: a warm, plain-English message that links each part of the offer to one of their priorities, says what happens next and when the written offer follows, and invites questions. Use British English and inclusive wording, and leave [brackets] for figures and names. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that nothing promised is outside policy or the approved band, that every claim about the role is true, and that the message reads as personal rather than templated.
Diligence: The hiring manager places the offer in the band using the same pre-set factors for everyone and makes the verbal offer personally. Figures and personal details are filled in by people, never pasted into AI.

Don't miss: Leaving it vague how much travel or customer-facing time the role involves.

Framework

Close plan (motivators → offer → objections)

Build the offer around what the candidate told you they care about, anticipate their objections, and make sure the verbal and written offers match.

List the candidate's top three motivators from your notes at every stage.
Make the verbal offer personally, and link each part of it to a motivator.
Ask what else they are weighing and when they have to decide.
Place the offer in the band using the same pre-set, job-related factors for everyone, and record why.
Send the written offer promptly after the verbal one.

Why it works: A close plan ties the offer to the candidate's stated priorities and makes approvals explicit before the final interview.

Offer checklist and negotiation

Stay inside the approved band. If the candidate is worth more, re-level them with evidence rather than stretching the band.
Trade in things that cost less than salary where policy allows: start date, learning budget, working pattern.
Before the written offer, ask: 'What would make this an easy yes?'
Agree approval and the pay band before the final interview, so the offer isn't held up.
The hiring manager makes the verbal offer; the written offer follows within a working day.
Record which pre-set factors placed the offer where it is in the band.
Tell unsuccessful finalists personally, without avoidable delay, with brief feedback.

What will close this candidate

The problem: they decide how AI fits into systems that matter, before costly mistakes are built in.
Authority: designs that teams build from, with a real say in when not to build at all.
Range: exposure to many customers' or teams' systems, rather than a single product's codebase.
Progression: what the senior or principal architect level looks like here, written down before they accept.

Compliance checkpointEUUS

Use objective, gender-neutral, pre-agreed factors to place candidates within the band, apply them consistently and record the decision as an internal pay-equity control. Check the national law implementing Directive (EU) 2023/970 and any local record-keeping rules; this recommendation is good practice, not a universal statutory documentation rule.

Sources: EU Pay Transparency Directive 2023/970 · checked 2026-09-25

Stage 12 of 13Required

0 of 2 done

Help them settle in

Pre-boarding and 30-60-90 onboarding

Who runs it: Hiring manager + buddy + HR
Done when: A new hire who has worked through a written 30-60-90 plan tied to the 12-month outcomes, with a check-in at each milestone.

Key tool

Before day one
Laptop, accounts and read access to existing architecture documents and decision records before day one.
Days 1–30: learn
Meets the engineering, security, and sales or product people who request, approve and build designs.
Days 31–60: contribute
Owns one design from request through to an approved written proposal.
Days 61–90: own
Has at least one design built, with its cost and latency compared with the original estimate.

AI as an advantage4D framework

Delegate: Hand AI the first draft of the 30-60-90 plan from the 12-month outcomes. Agreeing the plan with the new hire stays with the manager.

Show the prompt and the checks

Describe: You are helping a hiring manager draft a 30-60-90 day onboarding plan for a new AI Solutions Architect. Context: I will paste the 12-month outcomes from the intake meeting, the systems and people the hire will work with, and anything they will need before day one. Produce: a pre-boarding checklist (equipment, access, a named buddy, a first-week schedule); then three sections — days 1–30 learn, days 31–60 contribute, days 61–90 own — each with 3–4 concrete, checkable expectations and one milestone check-in question. Use British English and plain wording. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that each expectation is realistic for a new starter, ties back to a 12-month outcome, and offers the hire support rather than a test.
Diligence: The manager agrees the plan with the new hire in week one and owns the check-ins. The new hire's personal details stay out of the prompt.

Don't miss: Sending the new architect straight into a customer meeting to present a design they did not write.

Framework

30-60-90 day plan

Three phases — learn, contribute, own — with written expectations for each, agreed with the new hire in week one.

Before day one: equipment, access, a named buddy and a first-week schedule.
Days 1–30, learn: systems, people and the current state of the work.
Days 31–60, contribute: a first scoped piece of work shipped with support.
Days 61–90, own: an area of responsibility with clear outcomes.
Hold a check-in at each milestone against the written plan.

Why it works: Hiring doesn't end at the signature. A written onboarding plan makes expectations and support visible to the new hire and their manager.

Before day one
- Laptop, accounts and read access to existing architecture documents and decision records before day one.
- A summary of approved model providers, permitted regions and the data classification rules.
- A named partner in engineering who builds from designs, and one in sales or product who brings in requests.
- A short list of the designs currently in progress and who owns each.
Days 1–30: learn
- Meets the engineering, security, and sales or product people who request, approve and build designs.
- Reviews the live AI integrations and writes down, for each, its real data access, cost and fallback.
- Sits in on design conversations and contributes one written review of a colleague's proposal.
Days 31–60: contribute
- Owns one design from request through to an approved written proposal.
- Takes one design through security and privacy review.
- Drafts or updates a reference architecture the team can reuse.
Days 61–90: own
- Has at least one design built, with its cost and latency compared with the original estimate.
- Records one build, buy or fine-tune decision with its reasons for future teams.
- Presents a design to an executive audience and comes away with a clear decision.

Compliance checkpointUSUK

US: the employee completes Form I-9 Section 1 no later than their first day of paid work, and the employer completes Section 2 within three business days after that day (on the first day if the job lasts less than three business days). UK: complete the prescribed right-to-work check before employment starts, and apply it the same way to everyone.

Sources: USCIS — Form I-9 instructions · GOV.UK — Check a job applicant's right to work · checked 2026-09-25

Stage 13 of 13Required

0 of 3 done

Measure time to hire

Measure the process: mean time to hire

Who runs it: Recruiter / talent lead + hiring manager
Done when: A stage-by-stage timing record for this hire, the slowest step named, and one change agreed before the next requisition opens.

Key tool

Record when the hire accepts

•Time to hire: days from the successful candidate entering the pipeline (application or first contact) to the accepted offer. Average it across hires for mean time to hire.
•Time to fill, recorded separately: days from requisition approval to the accepted offer.
•Days per stage, from the stage's first action to its last — and whether the time was spent waiting or working.
•Offer-acceptance rate: offers accepted divided by offers made.
•Where candidates dropped out, and the reason you were given.
•How many days a senior reviewer took to mark each written design review after it arrived.

Check in at 90 days whether the hire is meeting the outcomes in the job description — that tells you whether the process picked well.

Read: mean time to hire Read: time to hire vs time to fill

AI as an advantageAI boost4D framework

Delegate: Hand AI the job of building a blank timing worksheet and the instructions for reading it. Entering dates, working out the timings and deciding what to change stay with the recruiter and the hiring manager.

Show the prompt and the checks

Describe: You are helping a recruiter and a hiring manager set up a timing review for an AI Solutions Architect hire. Context: I will list the stage names we used, in order (for example requisition approved, sourcing, screening, recruiter call, work sample, interviews, debrief, references, offer accepted). Produce: (1) a blank worksheet with one row per stage and columns for start date, end date, days in stage, days spent waiting (scheduling, approvals, no reply) and number of candidates who entered and left; (2) step-by-step instructions for us to work out, from our own records, time to hire (the successful candidate's entry to the pipeline, by application or first contact, to the accepted offer; average it across hires for mean time to hire) and, separately, time to fill (requisition approval to the accepted offer); (3) five questions we should ask about the slowest stage, separating waiting from working; (4) a short template for recording one change to make before the next hire. Use British English. Do not include benchmarks or example figures. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the worksheet covers every stage you actually ran, that the two definitions are not mixed up, and that the questions point at the process rather than at any person.
Diligence: People fill in the dates and do the arithmetic from their own records, choose the one thing to fix and write it down. No dates, names or details about any candidate go into the prompt.

Don't miss: Blaming drop-off on the written task without checking how many days the brief took to reach candidates after the recruiter screen.

Framework

Mean time to hire, by stage

Time to hire is the number of days from the successful candidate entering your pipeline (their application or your first contact) to their accepted offer; mean time to hire is that figure averaged across hires. Time to fill — requisition approval to acceptance — is a separate measure. Record both per stage, so you can see where the time went rather than only the total.

When the hire accepts, write down the date each stage started and ended, and the day the successful candidate entered the pipeline.
Work out the days per stage, the time to hire and the time to fill, and mark the slowest stage.
Note the offer-acceptance rate and the stage where most candidates dropped out.
Separate waiting time (scheduling, approvals, silence) from working time, and decide which one to shorten.
Fix one thing before the next hire, and write down what you changed.

Why it works: A single total hides where the delay sits. Per-stage timing points at the one step to fix, and the acceptance rate and drop-out points show whether candidates kept pace with the process.

Record for every hire

Time to hire: days from the successful candidate entering the pipeline (application or first contact) to the accepted offer. Average it across hires for mean time to hire.
Time to fill, recorded separately: days from requisition approval to the accepted offer.
Days per stage, from the stage's first action to its last — and whether the time was spent waiting or working.
Offer-acceptance rate: offers accepted divided by offers made.
Where candidates dropped out, and the reason you were given.
Check in at 90 days whether the hire is meeting the outcomes in the job description — that tells you whether the process picked well.

What to log for this role

How many days a senior reviewer took to mark each written design review after it arrived.
Which parts of the brief, systems diagram or constraint sheet prompted clarifying questions that the brief should answer next time.
How many candidates declined the written task, and at what point they dropped out.

Where this role's hiring slows down

Watch for marking of the written proposal waiting on one architect's calendar, with no second marker trained.
Watch for the hiring-manager round being booked before the proposal is marked, leaving the panel without specific questions.
Watch for customer commitments — travel, deal deadlines — repeatedly pushing back panel interviews.

Compliance checkpointUKEU

Under the EU and UK GDPR, tell candidates how long you will keep their data, or the criteria used to decide. Give that notice when you collect data from them directly, or within the Article 14 time limit when it comes from another source (such as a referral or a sourcing tool). Delete or anonymise data when it is no longer needed, keeping only what a documented lawful purpose still requires.

Sources: GDPR — Regulation (EU) 2016/679, Articles 5, 13 and 14 · ICO — UK GDPR guidance · checked 2026-09-25

General information, not legal advice. Rules depend on the employer, the role and where the work is done — confirm what applies with HR or counsel.

Frequently asked questions

What does an AI solutions architect do?

They design how an AI capability fits into existing systems: choosing whether to build, buy or fine-tune, laying out the architecture, working through data access and security review, setting cost and latency budgets, and planning fallbacks. Most of the output is written design and advice that engineers build from and leaders decide on.

AI solutions architect vs forward deployed engineer — what's the difference?

A forward deployed engineer works hands-on inside a customer's environment, writing and debugging the integration. An AI solutions architect designs the approach and advises on it — what to build, where data goes, what it will cost and what can go wrong — and usually hands the build to engineers. Many companies need both; hire for whichever of those jobs is actually missing.

How do you interview an AI solutions architect?

Give candidates a written design review based on a realistic brief, then run structured technical interviews on that proposal, on build-or-buy and budget decisions, and on explaining trade-offs to different audiences. Score every candidate against the same rubric before the panel compares notes.

What should an AI solutions architect job description include?

The systems the person will design around, the decisions they own, the few requirements that gate the job — a design that was built, written proposals, a data-access review — an AI-fluency section and a genuine pay range. Keep vendor certifications under nice to have.

Does an AI solutions architect need to code?

They need enough recent engineering experience to judge what a design will take to build and run, and to read what engineers produce. Writing production code is not usually the core of the job; if it is, you may be hiring a forward deployed engineer or an AI engineer instead.

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