Principal Engineer's Guide to AI System Design Interviews: Core Bottlenecks & Fundamentals
datawithsuman · x · 2026-08-12
A Principal engineer with 12 years of experience shares a comprehensive guide for system design interviews targeting Senior to Staff+ AI/ML roles. The author emphasizes that AI system design is fundamentally traditional system design, but with a shift in core bottlenecks.
Beyond standard databases and caches, AI system bottlenecks revolve around:
- Model Interaction: Tokens, context windows, and prompt design.
- Quality & Cost: Retrieval quality, inference costs, and hallucination management.
- Engineering Challenges: Model latency, routing, small vs. large model deployment, evals, and user trust.
Candidates must also master structured outputs, function/tool calling, agent memory, and guardrails.
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