Hire AI and ML Engineers
Helping you build your own AI capability — defining roles that match the work you actually have, assessing candidates on things that predict performance, and being honest about what your package and problems can attract.
Organisations write AI job descriptions listing every technology they have heard of, interview on algorithm puzzles, and then wonder why the person they hired cannot ship a model into production. The problem is almost always the definition and the assessment rather than the market.
This engagement supports you in hiring your own AI and machine learning engineers: defining roles against the work that exists, designing technical assessment that predicts on-the-job performance, structuring the interview process, and advising realistically on market positioning.
Define the role against the actual work
The most common hiring failure is recruiting for a role the organisation does not have. These are genuinely different jobs and conflating them wastes months:
| Role | Actually does | Hire when |
|---|---|---|
| ML engineer | Builds and productionises models | You have data and a modelling problem |
| Data engineer | Pipelines, quality, platform | The blocker is data access, which it usually is |
| MLOps / platform engineer | Deployment, monitoring, infrastructure | Models exist and operating them is the pain |
| Applied scientist | Novel methods, research adaptation | The problem genuinely lacks a standard solution |
| AI product engineer | Builds products on existing models | You are shipping LLM features, not training models |
| Data scientist | Analysis, experiments, insight | The need is understanding, not a production system |
Most organisations advertising for ML engineers need data engineers
If your models are blocked on access to reliable data — which is the usual situation — hiring someone to build models will produce a frustrated expensive person doing pipeline work they did not sign up for, who then leaves. We will say this at role definition even though it is not what people want to hear.
Assess for what the job requires
Use a realistic exercise, not a puzzle
A small, messy, representative problem with a written explanation of decisions and trade-offs predicts on-the-job performance far better than algorithm questions on a whiteboard. It also respects the candidate's time if kept to a few hours rather than a weekend.
Probe judgement, not recall
How they decided a model was good enough, what they would do differently, how they handled a project that failed. Strong candidates discuss trade-offs and their own mistakes readily; weaker ones describe only successes and cite benchmark numbers.
Test the things that actually bite
Data leakage, evaluation design, whether a baseline was established, what happens when the model degrades. These separate people who have shipped systems from people who have completed courses, and they rarely appear in standard interview loops.
Assess collaboration honestly
Most AI work fails at the boundary with the business rather than in the modelling. Can they explain a trade-off to a non-technical stakeholder, and can they hear that their model is not solving the real problem?
Be honest about what you can attract
Compensation, the interest of the problems, data availability and whether the person would be the only AI engineer all determine your realistic candidate pool. We will tell you when a role is not fillable as specified, and what would change that — usually the scope rather than the salary.
Keeping them once hired
- Give them data they can work with. The most common reason AI hires leave is spending a year on access problems nobody warned them about.
- Do not make them the only one. A single AI engineer with no peer to review their work is isolated and unlikely to stay long.
- Let their work reach production. People join to ship, and models that end as slide decks drive them out faster than compensation does.
- Provide the platform, or the time to build it. See MLOps implementation.
- Be clear about the research-to-delivery balance. Mismatch here is a frequent and avoidable reason for early departures.
We would rather help you build a team that stays than place people repeatedly, which is also why this engagement is advisory and fixed-fee rather than a placement commission.
How the engagement runs
Role definition comes first, because most hiring failures are definition failures.
Role definition
The work that actually exists mapped to the right role; realistic market positioning assessed.
Assessment design
Realistic exercise built from a representative problem; interview structure and scoring agreed.
Process support
Panel calibration, candidate assessment support and moderation of scoring.
Offer and onboarding
Positioning advice, and an onboarding plan that gets the person productive.
Retention setup
The conditions that keep the hire, including what needs fixing before they arrive.
What you receive
A role you can fill, an assessment that predicts performance, and conditions that keep the person.
Role definition
Mapped to the work you actually have, with the honest reassignment where it is a different role.
Market assessment
What your package, problems and data can realistically attract, and what would change it.
Technical assessment
A realistic exercise built from a representative problem, with scoring guidance.
Interview structure
Questions that probe judgement, and panel calibration.
Onboarding plan
What the person needs in their first ninety days to be productive.
Retention conditions
What to fix before they arrive, so they stay.
Is this the right engagement?
Worth being direct. Hire AI and ML Engineers is the wrong spend in some situations, and those are listed rather than buried.
Good fit if
- You are building an in-house AI capability rather than outsourcing it.
- Previous AI hires have not worked out and nobody knows why.
- You are unsure which role you actually need.
- Your interview process is not distinguishing candidates well.
- You want an honest read on what your package can attract.
Choose something else if
- You want a recruitment agency to source candidates — that is not what this is.
- The need is temporary. See staff augmentation.
- You want validation of a role definition already fixed.
- The organisation cannot provide the conditions a hire would need to succeed.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
Are you a recruitment agency?
No. We do not source candidates or take placement commission. We help you define the right role, design assessment that predicts performance, structure the process and set up the conditions that keep the person — on a fixed fee, because our interest should be in you building a team that stays rather than in filling seats.
Which role do we actually need?
Frequently not the one being advertised. Most organisations advertising for ML engineers need data engineers, because the real blocker is access to reliable data. Hiring a modeller into a pipeline problem produces a frustrated expensive person who leaves within the year — so role definition is where this engagement starts.
How should we assess AI candidates?
With a small, messy, representative problem and a written explanation of the decisions and trade-offs — kept to a few hours out of respect for the candidate's time. Then probe judgement: data leakage, evaluation design, whether a baseline was set, what they would do differently. Algorithm puzzles predict very little about shipping systems.
Can we compete for AI talent as a smaller organisation?
Often yes, on things other than compensation: interesting problems, real data, work that reaches production, autonomy, and not being the only AI person. We will tell you honestly when a role is not fillable as specified, and usually the fix is the scope rather than the salary.
Why do AI hires leave?
Most often because they spend a year on data access problems nobody warned them about, because their work never reaches production, or because they are the only AI person with no peer review. All three are fixable before the person arrives, which is why retention conditions are part of this engagement rather than an afterthought.
Often paired with this
Most clients combine two or three engagements from the Managed AI Services and Talent pillar. These are the ones that most often run immediately before or after.
AI Staff Augmentation
Individual engineers embedded in your teams, to your standards, with knowledge transfer expected.
Read more →Dedicated AI Development Teams
A complete team with its own lead and your product ownership, plus a transition plan from day one.
Read more →AI Managed Services and Retainers
Ongoing operation against defined service levels, with a documented exit to your own team whenever you want it.
Read more →Is this the right engagement?
Tell us what you are trying to build. If a different service fits better, or if you do not need us at all, we will say so.