Score candidates on five dimensions (fundamentals, AI evaluation, data and security, operations, ownership) and weight them by role.
The résumé problem with AI hiring
Familiarity with a model API or popular orchestration framework is not enough to establish production ownership. Strong candidates understand the failure modes, architecture boundaries, and measurable acceptance criteria of the systems they claim to build.
Dimension 1: engineering fundamentals
Ask the candidate to design a service boundary: API contracts, authentication, deployment strategy, caching, queuing, and failure handling. Look for clear trade-offs under changing traffic, partial failures, and operational constraints.
Dimension 2: AI-specific evaluation
Give candidates a flawed retrieval or agent workflow and ask how they would diagnose failure. Expect discussion of representative datasets, objective and human evaluations, latency, cost, reproducibility, and regression testing.
Dimension 3: data and security
Present a scenario with tenant-specific documents and sensitive actions. Good answers address document-level authorization, secrets, data provenance, logging minimization, and prompt-injection threats without treating the model itself as a security boundary.
Dimension 4: operational maturity
Ask what would appear on their dashboard during a production issue, how they would create incident runbooks, and which rollback options should exist. A strong engineer can explain what happens at 2 a.m. when the system stops working.
Dimension 5: communication and ownership
For embedded roles, candidates need to work in somebody else’s architecture and team rituals. Ask for an example where they disagreed about a design, clarified trade-offs, and shipped an outcome without creating a parallel process.
A five-point scoring rubric
Score each dimension on a 1–5 scale with anchored descriptions: 1 means cannot reason about basic constraints; 3 means sound approach with guidance; 5 means anticipates operational and business trade-offs with clear evidence. Weight dimensions by the specific role rather than averaging every category equally.
Frequently asked questions
Should every AI engineer interview include coding?+
A practical exercise can help, but it should reflect the real role. A data engineering interview needs different signals from a customer-facing FDE or agentic workflow engineer.
What is the strongest evidence of production experience?+
A precise explanation of decisions, failures, instrumentation, operational ownership, and measurable results is stronger than a list of frameworks or demo screenshots.
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