How to Assess AI Candidates: Technical and Non-Technical Skills

What Does It Mean to Properly Assess an AI Candidate?

Properly assessing an AI candidate means evaluating both their technical capabilities (such as machine learning knowledge, model deployment experience, and data engineering skills) and their non-technical competencies (including ethical reasoning, commercial awareness, communication, and learning agility) through a structured, role-specific framework. A candidate who excels on only one dimension is rarely a successful long-term hire.

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Key Takeaways

Before we go into detail, here is what this guide covers and what you should take away from it:

• Assessing AI talent requires a different framework to most technology hiring because the skill set is both broad and fast-moving
• Technical assessments should be role-specific, not generic. The right approach for a machine learning engineer differs significantly from what you need for an AI product manager or MLOps engineer
• Non-technical skills, particularly ethical reasoning, communication, and learning agility, are as important as technical ones and are frequently under-assessed
• The most reliable signals come from structured conversations about real work, not timed coding tests or abstract theory questions
• A formal AI hiring framework, applied consistently across every candidate, produces far better outcomes than ad hoc interview processes
• Specific red flags, including an inability to discuss failure, discomfort with uncertainty, and over-reliance on a single toolset, are worth knowing before you start

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Why AI Roles Demand a Different Interview Framework

Most hiring managers interviewing for AI roles are doing one of two things. Either they are leaning too hard on technical tests, treating the process like a coding exam, or they are winging the soft skills side entirely and hoping instinct fills the gap. Neither approach works particularly well when you are trying to hire someone who will shape how your organisation uses one of the most consequential technologies of our time.

Most interview frameworks were built for roles where the skill set is relatively stable. A software engineer from five years ago and one from today share most of the same fundamentals. AI is different. The landscape shifts dramatically, year on year. The tools, frameworks, and best practices that mattered in 2022 look quite different to those defining excellent practice today. A candidate who stopped learning eighteen months ago may already have meaningful gaps.

There is also the question of scope. AI roles are rarely purely technical. Machine learning engineers, data scientists, AI product managers, and AI ethics leads all require different balances of skills. A one-size assessment process will misfire across these variations.

The most effective AI hiring frameworks we see at Adria Solutions treat technical and non-technical assessment as equal pillars, not as a main event followed by a checklist.

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Assessing Technical Skills in AI Candidates

Step One: Define the Technical Depth You Actually Need

Before you write a job spec or design a competency-based interview for an AI role, you need to be clear about what you are actually hiring for. There is a meaningful difference between:

• A role that requires someone to build and train models from scratch
• A role that requires someone to fine-tune and deploy existing large language models or foundation models
• A role that requires someone to integrate AI APIs, tools, and automation into existing products and workflows
• A role focused on evaluating AI outputs, prompt engineering, and quality assurance

Each of these demands a different technical profile. Conflating them in your job spec leads to over-qualified candidates feeling underutilised or under-qualified candidates drowning in week three. Work with your technical lead to define the actual day-to-day requirements, not a wishlist of every AI term you have encountered recently.

Core Technical Skills to Assess by Role Type

Machine learning engineers and data scientists

The fundamentals remain important. You want to understand whether a candidate can reason about model architecture decisions, not just implement them. Ask them to walk you through a model they have built: the choices they made and what they would change with hindsight. The quality of their reasoning is more revealing than whether they can recite gradient descent from memory.

Assess their practical workflow. Can they talk about data pipelines, feature engineering, and the unglamorous work of cleaning and validating datasets? Strong AI candidates rarely romanticise their work. They will tell you about the time their training data had a subtle bias they missed on first pass.

Probe their evaluation methodology. How do they validate model performance beyond headline accuracy metrics? Candidates who only talk about accuracy, without mentioning precision, recall, F1, AUC, or appropriate business metrics, are giving you a signal worth noting.

MLOps and AI deployment engineers

Shift your focus toward infrastructure and production reliability. How does the candidate think about serving models at scale? What is their experience with model monitoring, retraining pipelines, A/B testing in production, and handling model drift? This is the less glamorous side of AI work, and the people who have genuinely done it will have specific, detailed answers. Generic answers about containers and orchestration, without any real texture about what broke and how they fixed it, suggest limited hands-on experience.

AI product managers and AI strategists

The technical bar here is different but still real. You are not looking for someone who can train a model. You are looking for someone who understands enough to challenge technical assumptions, identify when a proposed AI solution is over-engineered for the problem, and translate between engineering teams and business stakeholders.

Scenario-based questions work well. Give them a real business problem and ask how they would evaluate whether an AI solution is the right approach at all. Ask them to explain what makes a good evaluation dataset. If they have no view on that, it is a meaningful gap.

Prompt engineers and AI integration specialists

This is an emerging but increasingly important role type. Assess their understanding of model behaviour: how do they think about context windows, temperature, system prompts, and output consistency? Ask them to walk through how they would approach a prompt engineering task from brief to final output. Strong candidates approach this with rigour and iteration, not intuition alone.

Practical Technical Assessment Methods

Take-home tasks over timed coding tests

Timed coding tests replicate the pressure of an interview rather than the reality of the job. For most AI roles, a well-scoped take-home task produces better signal. Keep it bounded to three or four hours maximum, and make the brief as close to a real problem as possible without asking candidates to do unpaid consultancy work for you.

Portfolio and code review

Ask candidates to bring examples of previous work to the technical interview. Walk through it together. This is far more revealing than abstract questions because you can see how they think, how they document, and how they handle questions about their own decisions. Candidates who cannot discuss their own work with confidence are rarely a good sign.

System design discussions

For senior AI roles, a system design conversation replaces most of what a coding test would tell you. Ask a candidate to design an AI system for a given problem. You are evaluating their thinking, their ability to make trade-offs explicit, and how they handle ambiguity and constraints. The specific technical choices matter less than the quality of their reasoning.

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Assessing Non-Technical Skills in AI Candidates

This is where many AI hiring processes are weakest, and where the consequences of getting it wrong are often most significant.

Ethical Reasoning and Responsible AI

AI systems can cause real harm when built or deployed without sufficient care. Bias, privacy violations, opaque decision-making, and downstream societal effects are not abstract concerns. They are documented realities across healthcare, finance, hiring, and criminal justice.

The question is not whether a candidate agrees that AI ethics matters in principle. Of course they will say yes. The question is whether they have developed genuine ethical reasoning and can apply it to real situations under commercial pressure.

Ask candidates about a time they raised a concern about a project. What was the concern, how did they raise it, and what happened? Ask what they would do if they believed a model they were working on was producing biased outputs but the project timeline was tight. Listen for specificity, nuance, and a willingness to stand firm on something uncomfortable. Vague, rehearsed answers about responsible AI are a red flag. Specific, considered ones are a good signal.

Communication and Stakeholder Management

The ability to explain AI concepts to non-technical audiences is not a nice-to-have. It is a core professional requirement for most AI roles. Decisions about where and how AI is deployed are made at every level of an organisation, and those decisions are shaped by how well technical teams can communicate what they are building and why.

In the interview, ask the candidate to explain a technical concept as though speaking to a senior business leader with no technical background. Watch for jargon, watch for condescension, and watch for oversimplification that loses accuracy. The sweet spot is rare and genuinely valuable.

For senior AI hires, explore how they have influenced decisions in previous roles. Not just built things, but shaped thinking. This requires a different kind of presence and credibility than technical excellence alone.

Learning Agility

More than in almost any other technical domain, the ability to learn continuously is a genuine competitive differentiator in AI. Ask candidates how they keep their knowledge current. What have they learned in the last six months? What areas do they feel weakest in, and what are they doing about it? Strong candidates will have specific answers: particular papers, courses, tools, or communities. Candidates who speak only in generalities about staying curious may not have the habits to back it up.

Collaboration and Cross-Functional Working

Most AI work happens in cross-functional teams. Ask for examples of projects where the candidate had to work closely with people from very different backgrounds: product managers, data engineers, legal teams, marketing leads. How did they handle disagreements? How did they manage expectations when timelines slipped? The specific story matters less than whether the candidate demonstrates self-awareness and genuine adaptability.

Commercial Awareness

Even in highly technical AI roles, commercial awareness separates good candidates from great ones. Does the candidate understand how the work they do connects to business outcomes? Can they talk about trade-offs between model sophistication and cost? Have they ever pushed back on a technical approach because the business case was not there? An AI candidate who cannot think commercially will struggle to prioritise their work, communicate its value, or make sensible decisions when resources are constrained.

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Red Flags in AI Candidate Interviews

After placing AI talent across the UK for many years, certain patterns recur in candidates who do not work out.

They cannot explain what went wrong. Strong AI practitioners have extensive experience with failure. Models that did not generalise, projects that were cancelled, approaches that turned out to be the wrong fit. If a candidate gives you a clean narrative of continuous success, probe harder. Either they are shielding you from something important, or they have not taken on enough challenging work to have failed meaningfully.

They are uncomfortable with uncertainty. AI work involves operating under significant uncertainty. Candidates who speak only in certainties, who are unwilling to say “I do not know” or “it depends,” are often either overconfident or inexperienced. Both are concerning.

They cannot engage critically with AI. Ask a candidate to name the limitations or risks of a technology or approach they are enthusiastic about. How they respond tells you a great deal about their intellectual honesty and their ability to hold complexity without becoming defensive.

They over-rely on a single toolset. Strong AI candidates have conceptual understanding that transcends specific tools. If someone can only answer questions through the lens of one framework or one platform, that is a risk in a field where tooling evolves rapidly.

They have not thought about the downstream effects of their work. This is distinct from ethical reasoning as a topic. A candidate who has genuinely worked on consequential AI systems will have stories about things they noticed, worried about, or changed because of what they observed in production.

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Building a Structured AI Hiring Framework

Good candidate assessment does not happen by accident. It requires deliberate design.

Agree internally on what excellent looks like before you meet any candidates. Define the specific technical skills, the non-technical competencies, and the experience markers that genuinely matter for this role. Share that framework with everyone involved in the interview process.

Use consistent competency-based interview questions across all candidates for a given role. Inconsistent questions make comparison almost impossible and introduce unnecessary bias into your decision-making.

Score independently before discussing. If multiple interviewers compare notes before forming their own views, you end up with groupthink rather than genuinely diverse perspectives.

Include someone from the team the candidate will work in. Cultural and collaborative fit matters, and the people who can assess it most accurately are those who will work alongside this person daily.

Document your reasoning at the time, not retrospectively. Memory is unreliable and post-rationalisation is a real risk in high-stakes hiring decisions.

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A Note on AI-Assisted CV Screening for AI Roles

If you are using AI tools to screen CVs for AI roles, apply additional scrutiny to your process. Many strong AI practitioners are not optimising their CVs for keyword matching. Some of the most skilled people have unconventional career paths, academic backgrounds in adjacent fields such as mathematics, linguistics, or cognitive science, or portfolios that live on GitHub or in published research rather than in a formatted document. Over-reliance on automated screening can quietly filter out exactly the kind of creative, technically deep candidates you are looking for.

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Final Thoughts

Hiring great AI talent is one of the most consequential decisions a technology-driven organisation can make right now. The tools, frameworks, and applications are evolving faster than most hiring processes can keep up with. But the fundamentals of good assessment, clarity about what you need, consistent evaluation, genuine curiosity about how people think, remain entirely relevant.

The organisations getting this right are the ones treating AI hiring with the same rigour they bring to their most senior commercial appointments. The technical bar matters. But so does character, commercial instincts, and the ability to work thoughtfully with others in uncertain territory.

If you are building an AI team and want support designing an assessment process that actually works, or if you need access to qualified AI talent in the UK, get in touch with the Adria Solutions team. You may also find our guides on building an AI hiring roadmap for SMEs and what AI can and cannot replace in recruitment useful companions to this post.