Amazon AI GenAI Engineer Interview Questions

Hard Amazon company question — model cascades, unit economics, SageMaker/Bedrock routing, and SLO-aware cost optimization.

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

Company-wide context is a platform product.

Governance is becoming the bottleneck — not model IQ.

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

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

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.

Scenario interview on safe GenAI rollouts — flags, shadow eval, canaries, and kill switches.

Company-based MCP questions assess platform leadership, not wire protocols alone.

Dangerous tools are why agents aren't auto-root on production.

Long-running tools expose gaps between synchronous tool calling and real workloads.

Database MCP is high risk/high value — interviewers probe your safety instincts hard.

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

Hard project question on LLM gateway — auth, routing, rate limits, logging, key management, and multi-provider abstraction.

Project question on LLM routers — intent classification, cascades, early exit, and quality monitoring.

Scenario question on resilient LLM clients — exponential backoff, 429 handling, idempotency keys, and duplicate-safe writes.

Hard scenario on PII in LLM systems — detection, redaction, DPA compliance, log minimization, and regional deployment.

Hard project question on INT8/INT4 quantization, knowledge distillation, and serving smaller models for cost and latency.

Hard scenario on LLM cost control — token budgeting, routing, caching, batch APIs, and unit economics at millions of queries.