AI System Design Interview Prep
A production-focused guide for AI and machine-learning system design interviews, working through model selection, LLM gateways, caching, streaming, cost control and evaluation with decision tables and architecture diagrams rather than flashcards.
This guide targets senior, staff and principal engineers interviewing for AI platform or ML infrastructure roles, at companies from large tech firms to AI startups, fintech and SaaS companies. Each answer leads with a design decision, backs it with a trade-off table or diagram, and ends with the follow-up question an interviewer is likely to ask next, rather than presenting the material as flashcards.
An LLM Fundamentals module covers 24 questions on model selection, fine-tuning, prompting, serving, evaluation, context handling, multimodal models and alignment, and a System Design Patterns module covers 40 questions on gateways, routing, caching, streaming, cost, scaling, observability, guardrails, vector search, ingestion and capacity planning.
The project is the system-design hub of a small series; two companion repositories go deeper on retrieval-augmented generation, with over 200 questions on chunking, vector databases, hybrid search and reranking, and on agentic-system design patterns such as ReAct, tool use and multi-agent orchestration.