Google AI LLM Engineer Interview Questions

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.

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

**Session model** WebSocket/WebRTC streaming multimodal input/output.

**Flow** Multimodal intake → diagnose → suggest fix → verify resolved.

**Vertex settings** Safety filters on HATE, HARASSMENT, etc. with threshold BLOCK_ONLY_HIGH vs stricter.

**Hybrid** Traditional OCR for text layer + Gemini for semantic extraction.

**Model choice** Flash for simple; Pro for hard multimodal only when needed.

**Ingestion** Transcode standards; chunk very long media if API limits apply.

**API** Tool declarations with JSON schema; multi-turn functionResponse loop.

**Infrastructure** Managed endpoints vs batch prediction; GPU quotas.

**Mechanism** API retrieves search results, model grounds answer with sources.

**Gemini** Long video understanding, Google ecosystem integration.

**Use cases** Full codebase questions, hours-long media, multi-doc merger analysis.

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.

LLM interview topic on distillation — teacher/student training, latency/cost wins, and quality regression risks.

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.

A production AI coding agent typically includes:

AI observability extends classic APM.

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.

Scenario interview on defining and communicating hallucination metrics to executives.

Shared embedding spaces, separate indexes, CLIP-style models, metadata routing, and fusion strategies.

Cross-language prompt design covering translation drift, code-switching, locale formatting, and eval gaps in multilingual products.