How to Assess And Interview AI Engineers

In a rapidly evolving and highly competitive field, it has become increasingly challenging to assess AI candidates effectively. Hiring teams need AI recruitment processes and interview frameworks to evaluate AI engineers’ skills and capabilities, while distinguishing between candidates with strong technical expertise and those with limited real-world experience.

Jobs involving AI-related activities are projected to increase from around 158,000 in 2024 to 3.9 million by 2035, according to UK Government projections. As demand accelerates, developing robust AI recruitment processes will become an operational priority for organisations across industries. Establishing effective AI interview frameworks to support this is critical, as the cost of poor hiring decisions can be high given the complexity and strategic importance of AI projects.

By implementing strong processes that assess technical skills, portfolios, AI project experience, and soft skills, organisations can improve hiring outcomes, reduce risk, and secure the expertise needed to drive long-term innovation and business value.

Why assessing AI candidates is so difficult

Evaluating AI talent is challenging in part because the field is evolving so quickly that benchmarking skills and experience consistently is nearly impossible.

The sector is relatively new and many candidates have completed AI courses or built experimental projects, but lack experience deploying, scaling, and maintaining production-grade AI systems within real business environments, which is often what organisations most need. Therefore, hiring teams must distinguish between theoretical knowledge and practical experience.

For many businesses AI hiring is very challenging and organisations require assessment processes that go beyond qualifications and skills to evaluate their candidates’ abilities to solve real-world AI problems.

How to build an effective AI recruitment process

In order to hire AI engineers successfully, businesses need to implement a robust AI recruitment process. Organisations should first define the exact skills and experience required for the role before putting a strategy in place to assess their AI candidates’ technical expertise and soft skills. Companies can also evaluate candidates’ portfolios, AI project experience, and technical assessments to gain a clearer understanding of their capabilities and potential fit within the team.

Defining the type of AI engineers required

One of the biggest mistakes businesses make when hiring AI talent is failing to recruit the right AI professionals for their project and team. The term ‘AI engineers’ encompasses several specialist roles with distinct responsibilities and skill sets. Some of the common AI engineers include:

• ML (Machine learning) engineers who design, build, train, and optimise machine learning models, combining data science, and software engineering.
• Data engineers who build and manage systems that collect, store, clean, and process data. When working on AI projects, they maintain databases and ensure AI models have access to reliable and high-quality data.
• AI infrastructure engineers who design, build, and maintain the systems that allow AI models to be trained, deployed, and run reliably at scale.
• AI application engineers who build end-user products that have AI integrated in them.
• LLM engineers who build systems around large language models (LLMs) that make them work reliably and efficiently.

While smaller teams may rely on a few generalist AI engineers to cover multiple areas, it is still important for businesses to clearly understand the distinctions between different AI engineering specialisms.

Deciding between commercial, research, and deployment-focused AI engineers

Businesses should also clarify whether their new AI hire will be commercial, research, or deployment-focused.

Commercial AI engineers typically work on building AI features that support business or user needs. This contrasts with research AI engineers, who focus on improving model performance and developing innovative techniques, and deployment AI engineers, who specialise in taking models into production and ensuring they are scalable, reliable, and properly integrated into existing systems.

For example, startups might look for commercially minded engineers capable of rapid deployment to benefit their users and business needs, while larger organisations may prioritise scalability, governance, and infrastructure expertise.

By setting clear role definitions and priorities, businesses can hire more effectively based on project need and can evaluate AI professionals with far greater accuracy.

Designing an effective technical AI interview process

When hiring AI engineers, a structured recruitment process is highly recommended. Organisations need an AI interview framework that is efficient but can still accurately assess AI talent on their technical and soft skills when building their AI team.

A structured interview creates consistency in the process, making it easier for companies to evaluate AI engineers in depth.

The exact process may vary depending on whether the position is permanent or contract-based, as AI contract recruitment often requires a faster and more streamlined hiring approach. However, a typical AI technical interview process includes:

Initial screening

This usually involves a 30-45 minute vetting call focused on assessing the candidates’ communication skills, motivations behind looking for a new job, and gathering information on their previous experiences, as well as understanding their technical and soft skills.

Successful candidates typically demonstrate their sector-specific knowledge and experience with similar projects.

Technical interviews

A first-stage 60-minute technical interview should explore the candidates’ engineering abilities, AI and machine learning knowledge, system design thinking, and problem-solving approaches in much more depth.

Strong candidates are able to explain technical decisions clearly, discuss trade-offs, demonstrate structured thinking, and show practical understanding of building reliable and scalable AI solutions.

Technical AI engineer assessments

This stage is vital when evaluating AI engineers, as it can help confirm whether the candidates genuinely have the skills and experience they claim to have. To make the process more time-efficient, these assessments can often be completed on the same day as the technical interview.

Common AI assessment exercises include debugging AI workflows, coding assessments, prompt optimisation, evaluating model outputs, and designing AI automation solutions. However, businesses must be aware that the effectiveness of these assessments can vary depending on how they are structured and what they focus on. They should be reflective of the type of work organisations expect their AI professionals to perform.

Final stage interviews

Final-stage interviews are usually conducted by senior leadership. This final AI interview lasts roughly 60-90 minutes and focuses on the results from the technical assessment, combined with a much closer analysis of long-term fit. Discussions often centre around previous project deep dives, decision-making processes, stakeholder management, and business priorities.

Strong candidates typically demonstrate clear technical skill as well as key soft skills essential for teamwork.

Evaluating AI portfolios

AI portfolios are increasingly essential when hiring and assessing AI candidates. However, businesses must look beyond visually impressive projects and ensure the work demonstrates real substance, technical depth, and practical application.

When evaluating AI portfolios, companies should look for:

• End-to-end projects that go beyond deployment
• Measurable business outcomes
• Clear documentation with strong evidence of what the candidate did

The best candidates explain how they approached the problem, the reasons behind each of their decisions, and the trade-offs they had to make during development. They should also discuss projects that weren’t successful, what went wrong during development, or how they adapted when projects failed to improve the outcome. AI projects have a high failure rate, with a recent MIT report suggesting failures occur up to 95% of the time. Strong candidates admit to projects that didn’t succeed and discuss the learning points from them.

Companies also need to be aware that some AI candidates may include cloned projects in their portfolio. During the AI interview process, hiring managers should make sure to ask probing questions regarding the information provided in the portfolios.

Assessing AI technical skills

To effectively assess AI candidates’ technical skills, companies should prioritise evaluating their practical understanding and real-world experience during the interview. Strong AI specialists should demonstrate their ability to build, evaluate, and improve AI systems.

Core technical skills for AI engineers

• Programming expertise in one of the following: Python, R, Java, C++, and JavaScript
• Experience with AI frameworks and tools such as TensorFlow, PyTorch, LangChain, and OpenAI APIs, alongside modern application frameworks such as Node.js and React
• Understanding of machine learning and deep learning
• Strong data skills, including data preprocessing, SQL databases, ETL pipelines, and experience with large-scale data cloud platforms
• Model training, evaluation, optimisation, and performance tuning
• Prompt engineering and LLM application development

Key AI engineer interview questions to assess technical abilities

• How do you evaluate model performance?
• What are some risks of data leakage, and how do you prevent it?
• How do you debug training instability?
• Your model performs well in training but poorly in production. What are the possible reasons, and how would you investigate?
• How do you monitor machine learning systems in production?
• What metrics would you track to detect model drift or service degradation?
• How would you decide whether to use an LLM or a traditional ML model for a business feature?
• How do you measure whether an AI feature is delivering value to users and the business?

Evaluating AI engineers’ soft skills

While technical skills are essential for AI engineers, the strongest candidates have developed soft skills that can make a team successful.

The core soft skills AI engineers need

• Communication – AI professionals often have to explain complex projects and technology to a variety of non-technical stakeholders.
• Adaptability – AI projects have a high failure rate, but skilled and adaptable AI professionals can quickly identify when to change course to salvage a project.
• Curiosity – approaching AI projects in different ways motivates exploration and discovery, which often leads to performance improvements.
• Business awareness – commercial knowledge is essential for AI specialists as they need to build models that generate value for the organisations they work with.
• Ethical awareness – AI models may include potentially biased or sensitive information. AI engineers should understand how to prevent algorithmic bias or the divulging of sensitive information.

Common AI interview questions to assess soft skills

• How do you explain complex AI concepts to non-technical stakeholders?
• How did you handle changing project requirements or evolving business goals during a previous project?
• Describe a time when you had to defend a technical decision.
• Tell me about a time an AI project failed. What did you learn from it?
• How have you collaborated with others on a project previously?

How to assess machine learning engineers and large language model engineers

Under the term ‘AI engineers’, there are two specialist job roles that have gained popularity in the industry. ML (Machine Learning) and LLM (Large Language Models) engineers have much overlap but do diverge in several areas.

When evaluating LLM engineers, businesses should focus on asking LLM interview questions around prompt design, customisation, RAG architectures, and their ability to optimise systems that are reliable and cost-efficient.

In comparison, businesses should concentrate their Machine Learning interview questions on how well they can translate ambiguous business problems into measurable ML objectives, select appropriate models and metrics, and debug performance issues such as overfitting, data leakage, or distribution shift.

Common red flags in AI candidates for employers to look out for

Spotting red flags when companies interview and assess AI engineers is vital, as making poor hiring mistakes can be very costly. According to Glassdoor, UK AI engineers earn on average £58k a year as their base salary, though this varies significantly based on experience, industry, and seniority. Compensation can also be considerably higher for contractors, particularly those working under Outside IR35 arrangements with day rates.

Companies that make a bad hire have to go through the process of exiting them legally, which can take a long time, and then a new recruitment process needs to begin. Recruitment costs, delayed projects, and reduced team productivity can cause significant damage, meaning that understanding red flags is essential when hiring AI candidates.

Businesses need to look out for the following red flags:

• The CV/ portfolio is filled with buzzwords but no measurable outcomes
• They cannot explain previous experience in detail
• They overstate the success of previous projects and downplay failures
• They ignore ethical or security concerns
• They depend heavily on AI coding assistants
• They have a weak debugging understanding and shallow architecture reasoning

Key takeaways…

In a rapidly evolving industry, organisations need to identify AI professionals who have the right combination of skills and experience that will drive success in their AI projects. When conducting an AI engineer assessment, technical interviews, portfolios, and an evaluation of soft skills are all essential.

Furthermore, by standardising and clearly structuring interviews, businesses can improve the recruitment experience while ensuring candidates are assessed fairly and consistently. Ultimately, organisations that refine and standardise their AI hiring processes will be better positioned to secure top talent in a highly competitive market.

Can recruitment specialists help with AI recruitment?

AI recruitment specialists, like VIQU IT, offer access to passive networks, headhunting campaigns, and targeted recruitment that enables businesses to hire effectively in the sector. With the supply of qualified AI engineers low, specialist recruiters understand how to identify, assess, and secure top-tier candidates.

Want to find out more? Click here to get in touch with our team and discover how we can tailor our services to your business and connect you with skilled AI talent.