Senior AI Engineer Interview Questions

Enterprise RAG interviews test system design at scale: ACL-aware retrieval, audit logging, and ingestion pipelines for millions of documents. This is a staff-level question appearing at Microsoft, Salesforce, and Fortune 500 AI teams. Walk through a complete architecture with security boundaries and operational concerns.

Multi-agent orchestration is a senior/staff-level system design question gaining traction at Google DeepMind and Microsoft. Learn the supervisor pattern, shared state management, and how to avoid the 'too many agents' anti-pattern that sinks most candidate answers.

Self-hosting Llama 3 is a infrastructure-heavy question for ML platform and AI engineer roles at Meta-adjacent companies. Expect deep dives on quantization, vLLM, GPU sizing, and the TCO math that determines build vs buy decisions.

500M vectors at sub-100ms p99 is a staff-level vector search design question from Uber, Airbnb, and large-scale ML platform teams. Learn sharding strategies, index tuning, and the operational trade-offs that separate senior from principal engineers.

A production Claude Code rollout is an agent platform problem, not a 'give everyone a CLI' problem.

Cursor rules are context engineering in disguise.

Cost optimization for coding agents is context engineering + runtime policy.

This is an enterprise platform design question spanning agents, MCP, governance, and observability.

MCP multiplies agent power and attack surface.

Prompt injection is the #1 security interview topic for agents that read untrusted text.

Company-wide context is a platform product.

Repository configuration attacks target the files agents trust.

Governance is becoming the bottleneck — not model IQ.

Treat agent models/prompts/tools like dependencies.

Advanced scenario question on why LLMs hallucinate — next-token prediction vs fact verification, root causes, and production defenses asked at OpenAI, Google, Meta, Anthropic, Microsoft, and Amazon.

Part 2 of the LLM hallucination handbook — factual, citation, reasoning, math, code, temporal, context, and tool-use hallucinations; decoding risks; RAG failure modes; production case study.

Part 3 of the hallucination handbook — production defense layers from retrieval and prompting through guardrails, citations, confidence scoring, eval frameworks, and enterprise architecture. Asked at OpenAI, Google, Meta, Anthropic, Microsoft, and Amazon.

Hard RAG pattern question on corrective retrieval and gated web fallbacks.

Hard production LLM question on routing, cascading, and router evaluation.

Hard Llama/inference question on continuous batching and GPU efficiency.

Hard orchestration comparison — LangGraph agent graphs vs durable workflow engines.

Hard vector DB question on tenant isolation, noisy neighbors, and ACL safety.

Hard agents ops question on timeouts, circuit breakers, and budget protection.

Hard RAG ops question on index versioning, dual-write, and blue-green cutovers.