To interview an AI Engineer, test how they build production LLM features, design retrieval-augmented generation with vector search, engineer prompts, and evaluate non-deterministic outputs. Assess their judgment on guardrails and fallbacks, managing model-provider cost, latency, and rate limits, the fine-tuning versus prompting versus retrieval tradeoff, and whether they know where AI genuinely adds value versus risk.
Center the interview on production realities, since the hard part of AI engineering is reliability, evaluation, and cost, not calling an API. Strong candidates design evaluation before shipping, build guardrails for non-determinism, and treat a model as one component in a robust system. Watch for pragmatic judgment about appropriate use cases over hype-driven adoption.
Walk me through designing a retrieval-augmented generation pipeline for a knowledge-base assistant.
What to look for: Chunking, embeddings, a vector store, retrieval and re-ranking, and grounding the prompt in retrieved context. Addresses hallucination, citation, and freshness rather than just stuffing context.
How do you build evaluation for a non-deterministic LLM feature?
What to look for: Golden datasets, offline and online evals, LLM-as-judge with its caveats, and tracking quality over versions. Defines success criteria before shipping rather than eyeballing outputs.
When do you choose fine-tuning versus prompting versus retrieval?
What to look for: Retrieval for fresh or proprietary knowledge, prompting for behavior shaping, and fine-tuning for style, format, or narrow tasks at scale. Weighs cost, maintenance, and data needs.
How do you manage cost, latency, and rate limits when integrating model-provider APIs?
What to look for: Model selection by task, caching, batching, streaming, token budgeting, and backoff on rate limits. Treats cost and latency as first-class engineering constraints.
How do you design guardrails and fallbacks for unpredictable model behavior?
What to look for: Input and output validation, schema or tool constraints, content filtering, retries, and graceful degradation when the model fails. Plans for the model being wrong, not just right.
Walk me through iterating on a prompt that is producing inconsistent results.
What to look for: Systematic changes tested against an eval set, structured output, examples, and decomposition. Disciplined iteration rather than random tweaking until it looks fine.
Tell me about an AI feature you shipped to production. What made it reliable?
What to look for: A real feature with evaluation, guardrails, and monitoring, and the engineering that made it dependable. Beyond a demo to a maintained system.
Describe a time an AI feature behaved badly in production. How did you respond?
What to look for: Detecting the issue through monitoring, diagnosing drift or a prompt or data problem, and fixing it with a preventive measure. Honest about the failure mode.
Tell me about a case where you decided AI was not the right solution.
What to look for: Pragmatic judgment that a deterministic or simpler approach fit better, given cost, risk, or reliability. Resists applying AI for its own sake.
Give an example of optimizing an AI feature's cost or latency without hurting quality.
What to look for: Concrete levers like caching, smaller models for easy cases, or prompt trimming, validated against evals. Measures the tradeoff rather than guessing.
Users report the assistant confidently gives wrong answers. How do you diagnose and fix it?
What to look for: Checking retrieval quality, grounding, prompt, and model, adding citations and guardrails, and evaluating the fix. Targets hallucination at its source.
A new feature must answer from constantly changing internal data. How do you architect it?
What to look for: Retrieval over fine-tuning for freshness, an indexing and update pipeline, and grounding with source attribution. Keeps answers current and verifiable.
Your model-provider costs are growing faster than usage. How do you investigate?
What to look for: Profiling token usage by feature, finding expensive prompts or oversized context, and applying caching or cheaper models where safe. Cost engineering backed by data.
Product wants an AI feature you think is risky or low-value. How do you respond?
What to look for: Articulating the risks, proposing a scoped experiment with evaluation, or a non-AI alternative. Honest, evidence-based pushback rather than blind enthusiasm.
Output quality silently degrades after a provider updates their model. How do you detect and handle it?
What to look for: Continuous evals catching the regression, pinning or testing model versions, and a rollback or prompt fix. Treats model updates as a managed risk, not a surprise.
How do you set realistic expectations with product and stakeholders about what AI can and cannot do?
What to look for: Clear communication about non-determinism, accuracy limits, and evaluation results. Manages hype and aligns scope to what is reliably deliverable.
How do you work with the wider engineering team to integrate AI into a robust system?
What to look for: Treating the model as one component with clear interfaces, error handling, and observability. Collaborative, sound software engineering around the AI.
How do you stay current in a fast-moving field without chasing every new model?
What to look for: Evaluating new tools against real needs and adopting selectively. Pragmatic discernment over hype-driven churn.
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