Hard

Interview

How do you decide which tools to give an AI agent, and how many is too many?

Tool design is the main lever for agent reliability — bad tool sets cause wrong calls, loops, and security holes.

Interview

How would you design an AI agent that books meetings on behalf of a user?

Tests agent design fundamentals — tool design, safety, reliability under autonomy.

Interview

Describe a time when you raised an ethical concern about an AI feature.

Increasingly common in AI interviews. Tests judgment, courage, and ability to navigate ambiguity.

Interview

Tell me about shipping an AI feature when you had limited or messy data. What did you do?

Tests pragmatism — most real AI work starts without a perfect dataset; interviewers want judgment, not paralysis.

Interview

How do you defend a production LLM application against prompt injection attacks?

Tests whether you treat LLM security as layered engineering, not a single prompt trick.

Interview

Walk me through designing a production RAG system for a docs chatbot.

System-design-style question that's now standard in AI engineering interviews.

Interview

Design a routing layer that picks the right LLM per request to minimize cost while meeting quality SLAs.

Tests pragmatic platform design — frontier models on every request doesn't scale; routing is how teams ship profitably.

Interview

Design an internal platform for running LLM evals at scale.

Tests AI infrastructure thinking — increasingly relevant as companies build AI platforms.

Interview

Design a multi-tenant RAG platform where each customer uploads private documents.

Tests isolation, scale, and SaaS thinking — the shape of most enterprise AI products.

Interview

Design a gateway that screens LLM traffic for prompt injection and unsafe tool use.

Tests security architecture for AI platforms — increasingly required for enterprise AI gateways.

Interview

Design a real-time fraud detection system for credit card transactions.

Classic ML systems question — tests latency awareness, feature engineering, and pragmatism.

Interview

Design a recommendation system for a video platform (like YouTube) with 1B users.

Classic ML system design — tests scale, latency, model choices, and pragmatism.