How to Interview for GenAI and LLM Engineering Roles

A surprisingly reliable early filter is asking a candidate how they evaluate whether an LLM-based feature is actually working well — what metrics they use, how they handle the subjectivity of output quality, and how they've built regression testing for a system whose outputs aren't deterministic. Candidates with genuine production experience have thought deeply about this problem; candidates who have only prototyped tend to give a vague or generic answer.

Rather than a whiteboard architecture question, a stronger exercise presents a small, realistic retrieval-augmented generation pipeline or prompt chain and asks the candidate to identify what they'd change and why — perhaps a chunking strategy that would degrade retrieval quality, a prompt structure vulnerable to injection, or a missing fallback for a low-confidence response. This tests applied judgment far more directly than an abstract systems design discussion.

LLM-based systems fail differently than traditional software — hallucination, inconsistent outputs, and prompt injection are categories of failure most engineers haven't dealt with before working on generative AI systems specifically. A strong candidate can describe concrete strategies they've used to detect and mitigate these failure modes in a live system, not just acknowledge that they exist in theory.

Production LLM applications frequently need to balance model quality against cost and response latency — a larger, more capable model isn't always the right choice once real usage volume and cost constraints are considered. Candidates who can reason through this tradeoff, including strategies like model routing or caching, demonstrate a level of production maturity that pure prototype experience rarely develops.

It's tempting to focus an entire interview loop on AI-specific judgment, but GenAI engineers still need solid software engineering fundamentals — testing discipline, code quality, and the ability to build reliable, maintainable systems around the AI component. Skipping this assessment because a candidate demonstrated strong AI-specific judgment is a common oversight that shows up later as technical debt.