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Practice the modern AI/LLM engineering interview: RAG pipelines, embeddings and vector search, fine-tuning vs prompting, agents and tool use, evals, inference cost, and production safety.
Modern AI engineering loops test RAG design (chunking, retrieval quality, reranking, groundedness), embeddings and vector search (similarity metrics, ANN indexes like HNSW), when to prompt vs fine-tune vs retrieve, agent and tool-use architectures, evaluation (golden sets, LLM-as-judge), inference economics (context windows, KV cache, prompt caching, batching), and production safety (prompt injection, PII handling, rate limiting, fallbacks).
Classic ML interviews test model training: bias-variance, regularization, metrics like ROC-AUC. AI engineering interviews assume a capable foundation model and test how you build systems around it - retrieval pipelines, tool-calling agents, eval harnesses, cost and latency budgets, and defenses against failure modes like hallucination and prompt injection. It is closer to system design than to statistics.
You need working depth, not research depth: what attention does at a high level, why long contexts are expensive, how tokenization affects cost and behavior, what temperature and top-p actually change, and what the KV cache is. Deriving backpropagation is rarely asked; explaining why long prompts are slow and expensive is.
Expect scenario questions: how to chunk documents (fixed-size vs semantic vs structural), how to measure and improve retrieval quality (recall@k, hybrid search, rerankers), how to reduce hallucination (grounded citations, refusing to answer when retrieval is weak), and when RAG beats fine-tuning (fresh or per-tenant knowledge, auditability). Being able to debug a bad RAG answer end-to-end is a strong senior signal.
Yes - evals are a favorite differentiator. Know golden/regression sets, task-specific metrics (exact match, groundedness, pass@k for code), LLM-as-judge and its pitfalls (position bias, self-preference, verbosity bias), pairwise vs absolute scoring, and why you re-run evals on every prompt or model change like a CI suite.
Prompt injection (especially indirect injection through retrieved or fetched content), PII redaction before sending data to third-party APIs, per-user token budgets and rate limits to cap spend, graceful degradation when the model provider is down or slow, streaming for perceived latency, and logging/tracing of prompts and completions with sensitive data scrubbed.