Google AI AI Engineer Interview Questions

RAG has become the foundational architecture for production GenAI applications at companies like Notion, Duolingo, and Morgan Stanley. Interviewers expect you to explain the full retrieval pipeline — not just define the acronym. Follow along to master what RAG is, when to use it over fine-tuning, and how to articulate trade-offs that separate junior from senior candidates.

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.

Google's Gemini 1.5 Pro long-context window opens use cases impossible with standard LLMs — whole-codebase analysis, multi-hour video, massive document review. Interviewers test whether you understand real limitations behind the 1M token marketing number.

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

A production AI coding agent typically includes:

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

MCP (Model Context Protocol) defines a clean separation:

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.

Product teams shouldn't each reinvent agent security.

AI observability extends classic APM.

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.

Scenario interview on defining and communicating hallucination metrics to executives.

Medium Gemini/Vertex question on context caching vs RAG trade-offs.

Medium vector DB / embeddings trade-off question on dimensions vs cost/quality.

Medium multimodal prompting question for UI bug diagnosis.

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

Medium RAG interview on hard negatives for embedding/reranker quality.