Amazon·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

Senior

Jul 2026

Summary

System design round at Amazon for an ML Engineer role, focused entirely on building a production RAG system for enterprise text. One long question that basically covered everything from ingestion to monitoring, which sounds manageable until you're actually in it trying to not forget half the components.

Questions Asked (1)

Design a retrieval-augmented generation system for enterprise text, covering the ingestion pipeline, retrieval strategy, prompt orchestration, grounding and citations, freshness handling, latency and throughput targets, privacy controls, hallucination reduction, evaluation approach, and production scaling and monitoring.

System DesignTechnical Trade-offsProduct Analytics & Metrics

Author's notes

This is basically a full ML systems design question crammed into one prompt.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (data volume, query types, latency SLAs, privacy needs) to scope the design. Then walk through the end-to-end pipeline: ingestion, retrieval, generation, and evaluation, making explicit trade-offs at each stage. Emphasize grounding, citations, and monitoring as first-class concerns, not afterthoughts.

Pro tip: Anchor your design in measurable business outcomes (e.g., answer accuracy, latency, cost per query) and proactively discuss failure modes and mitigations—this shows production maturity beyond just architecture.

1. Clarify Requirements and Constraints

Ask about data scale, query types, latency/throughput targets, privacy regulations, and existing infrastructure. This ensures your design is tailored and avoids over-engineering.

2. Design Ingestion and Indexing Pipeline

Outline document parsing, chunking, embedding, and indexing. Discuss trade-offs in chunk size, embedding models, and metadata enrichment for citations and freshness.

3. Define Retrieval and Ranking Strategy

Choose hybrid retrieval (dense + sparse) with re-ranking. Explain how to handle freshness (e.g., time-decay scoring) and ensure diversity and relevance.

4. Orchestrate Generation with Grounding and Citations

Describe prompt construction with retrieved context, citation injection, and hallucination reduction techniques (e.g., constrained decoding, self-check). Address privacy controls like PII redaction.

5. Plan Evaluation, Scaling, and Monitoring

Propose offline and online evaluation metrics (faithfulness, answer relevance), A/B testing, and production monitoring for latency, throughput, and drift. Discuss scaling strategies (caching, sharding).

Key Points to Mention

Hybrid retrieval combining dense embeddings and sparse BM25 for robustness
Chunking strategies with overlap and metadata for precise citations
Freshness handling via incremental indexing and time-aware ranking
Hallucination mitigation: grounding prompts, citation enforcement, and answer verification
Privacy controls: data anonymization, access control, and encryption at rest/in transit
Evaluation framework: human-in-the-loop, automated metrics (e.g., RAGAS), and production monitoring

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.