20/05/2026
Interview rooms for AI engineering are converging. Same four questions, different wrappers, every panel I have been part of in the last six months.
If you can answer these four cleanly, you are deep into senior IC compensation territory regardless of what your title was 18 months ago.
Walk me through a RAG you have shipped. The interviewer is listening for whether you have actually built one. Specifics win: how you chunked, why you picked that embedder, what your reranker added, how you evaluated, what you would change next time. Vague answers ("we used LangChain") immediately drop you out of the senior bucket.
How would you evaluate this agent in production. The eval question is the new system-design question. The interviewer wants to hear about offline goldens AND online metrics, what you would label, how you would catch regressions, what you would alert on. Bonus points for talking about cost-per-call drift, not just latency.
Design an LLM gateway with fallbacks. The system-design classic, AI flavor. Rate limits per tenant. Retries with backoff. Multi-provider routing. Cost ceilings. The shape is "how would you build a production-grade wrapper around the model APIs." If you have built one, this is your softball question.
Your prompt regressed overnight. Trace through what you check first, second, third. The on-call question. The interviewer is testing whether you have actually debugged AI in production. They want to hear about traces, prompt versioning, eval gates, and the rollback path. The wrong answer is "I would check the prompt." The right answer is "I would check the trace for the failing query and diff the prompt version against the last known good."
Build the production AI engineering stack interviews are actually looking for:
learnwithparam.com/ai-bootcamp
