Posted about 12 hours by Jonny Grange
Hiring AI professionals is not the same as hiring for other technical roles. The skills needed are diverse, the job market is competitive, and the cost of a wrong hire can be significant. Without a clear framework for assessment, employers risk bringing in candidates who either lack the depth of expertise or cannot translate technical work into business impact.
Assessing AI candidates effectively requires more than checking technical knowledge. You need to evaluate problem-solving ability, real-world delivery, and how well someone can work with both technical and non-technical teams.
In this blog, we explain how to assess AI candidates, highlight common mistakes, and share practical steps you can apply to improve your hiring decisions.
Why assessing AI candidates is different
AI hiring is not straightforward. Unlike many roles where skills are easier to define, AI covers a wide range of specialisms and levels of expertise. This makes it harder for employers to know what to prioritise when assessing candidates.
The breadth of AI specialisms
Artificial intelligence is a broad field. A candidate may specialise in machine learning, natural language processing, computer vision, or data engineering. Each requires a different set of skills and tools. The challenge is understanding whether a candidate’s expertise matches the actual needs of your business. Hiring someone with the wrong focus can lead to misaligned projects and wasted investment.
Separating hype from proven experience
AI is full of buzzwords and inflated claims. Candidates may present themselves as experts, but without clear evidence, it can be difficult to judge whether they have delivered real results. Employers need to look for tangible examples of projects, outcomes, and business impact rather than being swayed by technical jargon.
Balancing technical depth with business impact
It is not enough for an AI candidate to be highly technical. They also need to show how their work connects to business goals. The best hires demonstrate both technical ability and an understanding of how AI adds value in practice. This balance is essential if you want projects to move beyond experiments and deliver measurable results.
Core skills and qualities to look for
When assessing AI candidates, it is important to look beyond technical ability alone. Strong hires combine expertise with commercial awareness and the ability to work well with others.
Depth in relevant technical expertise (and how to verify it)
AI roles can require knowledge of algorithms, programming languages such as Python, frameworks like TensorFlow or PyTorch, and experience with data pipelines. The key is ensuring their skills match the specific needs of your projects. Ask for details of the tools they have used, the size and complexity of datasets they have worked with, and how their models were deployed in real-world settings.
Ability to translate AI into business outcomes
The most effective AI professionals can connect their technical work to commercial value. Look for candidates who can explain how their solutions solved a problem, increased efficiency, or improved customer experience. This shows they are focused not only on building models but also on delivering outcomes that benefit the business.
Evidence of delivering scalable solutions
AI projects often fail when models work in testing but cannot scale to production. Ask candidates about their experience deploying models into live environments, monitoring performance, and adapting systems as requirements changed. Proven experience in delivering at scale suggests they can contribute beyond experimentation.
Collaboration and leadership potential
AI rarely exists in isolation. Successful projects require collaboration with product teams, data engineers, and business stakeholders. Strong candidates show they can communicate clearly with non-technical colleagues and, where relevant, lead others in a project setting. Leadership potential is particularly important if you are building or expanding an AI team.
Practical ways to assess AI candidates
A structured assessment process helps you compare candidates fairly and reduce the risk of making the wrong hire. Combining technical checks with behavioural evaluation gives you a well-rounded view of both ability and potential.
Structured technical interviews (what to test and why)
Technical interviews allow you to test a candidate’s knowledge in a focused way. Rather than relying on vague questions, prepare a set of structured topics that relate directly to the role. For example, you might explore their understanding of machine learning algorithms, data preprocessing, or model evaluation techniques. This ensures you are testing for depth rather than surface-level familiarity.
Reviewing project portfolios and case studies
A portfolio of past work provides clear evidence of a candidate’s ability. Ask them to walk you through a project, explaining the challenge, their role, the methods they used, and the outcome. Case studies reveal not only technical skills but also how the candidate thinks about business impact, which is often more important than the technical detail itself.
Problem-solving tasks and coding challenges
Short tasks or coding challenges are useful for testing practical skills. These should reflect the kind of problems they will encounter in your organisation rather than artificial puzzles. For example, you could ask them to clean a dataset, build a simple model, or suggest improvements to an existing approach. This shows you how they think under realistic conditions.
Behavioural and situational interview questions
Technical ability is only part of the picture. Use behavioural and situational questions to explore how a candidate approaches challenges, works in teams, and communicates with stakeholders. Questions such as “Tell us about a time you had to adjust an approach after feedback” or “How do you explain complex results to non-technical colleagues?” reveal important qualities for long-term success.
Common red flags to avoid
Identifying warning signs early prevents wasted time and helps you avoid hiring someone who cannot deliver on the role. When assessing AI candidates, be alert to the following issues.
Over-reliance on buzzwords without depth
AI has its fair share of popular phrases, but strong candidates should be able to explain the practical detail behind their work. If someone talks about machine learning or neural networks but cannot describe which algorithms they used, how they managed the data, or how they evaluated success, this suggests limited hands-on knowledge. Dig deeper with follow-up questions to confirm real expertise.
Inability to explain decisions clearly
AI professionals must communicate complex topics in a way that both technical and non-technical colleagues understand. If a candidate struggles to explain why they chose a certain model, how they mitigated bias, or how they validated results, it could indicate they were not closely involved in the project or lack the ability to engage with wider business stakeholders.
Lack of end-to-end project experience
AI projects involve more than model building. They require data preparation, deployment, monitoring, and iteration. Candidates who have only worked on a narrow slice of the process may not be able to manage projects independently or contribute effectively in smaller teams. Look for experience across multiple stages, even if at different levels of responsibility.
No evidence of measurable impact
AI should be tied to outcomes that support business goals. Candidates who cannot show how their work improved accuracy, reduced costs, streamlined processes, or created new opportunities may have focused on experimentation without impact. Always ask for quantifiable results, even if approximate, to confirm value creation.
Improving your assessment process
A structured and consistent assessment framework helps you reduce bias, improve hiring decisions, and compare candidates fairly. Below are ways you can strengthen your process when assessing AI candidates.
Building structured evaluation criteria
Define in advance what you are assessing for, and make sure every interviewer is aligned. This should cover technical depth, problem-solving ability, collaboration, and business impact. A scoring system for each category keeps assessments consistent and avoids decisions being driven by gut instinct. It also allows you to compare candidates objectively, especially when interviewing multiple people for senior roles.
Involving cross-functional stakeholders
AI projects rarely sit in isolation. They often involve collaboration with product, engineering, data, and business teams. Including representatives from these functions in the assessment process helps you evaluate how well a candidate can work across boundaries. It also provides a broader perspective on whether the candidate’s approach fits the culture and priorities of your organisation.
Using specialist recruiters to pre-qualify candidates
Recruiting AI talent requires expertise in both the technical landscape and the market for skilled professionals. Partnering with a specialist recruitment agency, like us, ensures candidates are pre-screened for both technical skills and team fit. This saves internal teams time and provides confidence that the shortlist already contains people who meet your requirements.
Continuously refining based on hiring feedback
No process should remain static. Collect feedback after every hire on what worked, what challenges were faced, and whether the new hire matched expectations.
Over time, you can adjust interview formats, evaluation criteria, or the weighting of certain skills. Continuous refinement improves the accuracy of your assessments and reduces the likelihood of a mis-hire.
Assessing AI candidates requires more than just reviewing technical skills. It means balancing expertise with business impact, team fit, and the ability to deliver real results.
By following a structured process and knowing what to look for, you increase your chances of hiring AI professionals who will make a long-term difference.
If you are looking to hire AI talent, get in touch with Digital Waffle. As a specialist AI recruitment agency, we connect employers with pre-assessed candidates who bring both technical depth and business value.
