Q: What do AI Engineer interviews actually test?

Three areas, usually tested through one vehicle: your own project.

AI foundations. LLMs, RAG, tool calling, embeddings. Do you understand what's under the hood, or only the framework on top of it?

AI architecture decisions. Which model, which orchestration approach, which framework — and why. This is where they test judgment, not knowledge.

Production deployment. System design, trade-offs, evals, security. The area that separates senior candidates from tutorial graduates, because you can't fake production experience you've never had.

The vehicle for all three: "walk me through your system." Interviewers love this question because it tests everything at once and can't be memorized. Why this vector database? What breaks at scale? How would you know if quality degraded next month? If you built your projects through real trial and error, these answers come naturally. If you copy-pasted, you stall on the first why.

And beyond the technical rounds: your story. Why this transition, why now, how your previous career feeds this one. Interviewers filter on communication and attitude more than candidates expect, so prepare the story with the same seriousness as the stack.

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