Designing systems where the model is the hard part. Start with how the round differs and the estimation that decides your architecture, then the reusable machinery — vector search, feature stores, two-stage ranking, LLM serving, retrieval, evaluation, guardrails — and work through the problems interviewers actually ask, from recommendation and fraud to assistants and agents.
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How an AI system design round differs from a classic one: turning an open business goal into a measurable objective, choosing metrics that survive contact with production, and the token, GPU and cost arithmetic that decides the architecture before you draw it.
The reusable pieces every AI system is assembled from — embeddings and vector search, feature stores, two-stage retrieval and ranking, LLM inference serving, the RAG pipeline, evaluation, guardrails, and the cost and latency levers that decide whether any of it ships.
The classic machine learning problems that still dominate the round: recommendation, search ranking, click-through prediction, fraud detection, and content moderation at scale.
Applications built on large language models — conversational assistants, support bots, coding tools, enterprise retrieval — and the inference platform that has to serve them affordably.
Systems whose components act on their own: personal assistants, web agents, multi-agent pipelines, and self-improving optimization loops — where the design has to assume the components are sometimes wrong.
