Separate research depth from production readiness
AI work spans research, applied modelling, platform engineering and product delivery. A role may touch several areas, but assessment improves when the team identifies which decisions the hire will own. Asking a candidate to build a model, design infrastructure and set product policy in one short exercise rarely reveals where their judgment is strongest.
Discuss the candidate's contribution to a project in detail. What alternatives did they consider? Which assumptions did they test? How did they handle latency, cost, privacy or a failure mode? The answers show how they connect technical work to the environment in which it must operate.
Look at data and evaluation choices
A reliable system begins with a careful definition of the task and the data available to address it. Candidates should be able to discuss data quality, leakage, representativeness and the limits of their evaluation set. A metric is only useful when it reflects the outcome the team needs and the errors people can tolerate.
Ask how performance was checked across different use cases and what happened when the system failed. Good practitioners can describe not only a successful result but the uncertainty around it. That ability matters when a prototype is moving toward a customer-facing or operational setting.
Test production thinking
Production AI involves software fundamentals alongside model choices: observability, versioning, fallback behaviour, security, cost controls and a path to rollback. A practical interview can ask a candidate to sketch a system or review a realistic design scenario, then explore the trade-offs that shape their answer.
The interview should match seniority. An early-career engineer may show sound debugging and testing habits; a principal candidate should explain how standards, interfaces and review practices support multiple teams. The point is to understand the level of ownership the person has exercised.
Include responsible judgment
Responsible deployment is part of technical judgment. Explore how candidates identify sensitive data, consider misuse, document limitations and involve colleagues who understand legal, security or user needs. No single hire can own every risk, but people building AI systems should be willing to surface issues and work across disciplines.
Close by explaining the realities of the role: research freedom, evaluation expectations, infrastructure constraints and how success will be measured. An honest description helps experienced candidates decide whether the opportunity fits their strengths and values.
Frequently asked questions
Should every AI engineer complete a coding test?
Use an assessment that fits the role. A short, job-related exercise or design discussion may provide better evidence than a generic timed test.
What separates an AI prototype from production experience?
Production work adds reliability, monitoring, security, cost management, user context and clear handling of failure alongside model performance.