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The AI engineer role is distinct from the ML researcher and the MLOps engineer: you build production systems on top of LLMs - RAG pipelines, agents, evals, inference services - and interviews test that applied systems judgment, not just model theory. This path sequences the AI-specific question bank, coding, and LLM system-design walkthroughs into an interview-ready progression.
The core of the modern AI interview: retrieval, embeddings, prompting-vs-fine-tuning-vs-RAG, agents and tool use, evals, and inference cost/latency. If any of these are fuzzy, this is where to spend time - the rest of the loop assumes this vocabulary is reflexive.
AI-engineer loops still include a coding screen. These lock in the patterns that show up most: hashing, heaps for top-k retrieval ranking, graph traversal for dependency/agent graphs.
The AI system-design round centers on inference and retrieval infrastructure. Walk the LLM inference/RAG platform end to end, then the adjacent primitives - model serving, autocomplete/semantic search, and the caching layer that dominates cost.
AI-lab loops probe safety judgment, rigor when ground truth is unknown, and truth-seeking over conviction. These themes rehearse exactly those signals.
The prompt-engineering and LLM sheet is the fastest way to pick up the shared vocabulary these interviews assume; the distributed-systems sheet covers the serving half.
AI-900 covers the applied-AI fundamentals ground that generalist AI-engineering screens tend to assume.
21 role-targeted paths are live, from new-grad and backend through SRE, security, data and engineering management. If you have a role you want covered, let us know.
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