System design & interview — A separate rail — not about building, about being hired

This track is honestly part of the path I walked, but it is a different kind of thing to the six above: it is about communicating the work rather than doing it. Pick it up at the end, or in parallel once you have something built to talk about. Nothing here helps you if the previous tracks are empty.

Track 6 of the AI engineering roadmap by K Laxman — about 8 hours across 2 stages.

System design fundamentals, then real problems

You can hold a 45-minute design conversation without hand-waving the storage layer.

storage · scalability · networking & latency · fault tolerance · CAP · databases · Redis · Kafka · load balancers · CDNs

You're done when: You can design a system that includes an LLM without the LLM becoming a magic box in the middle of the diagram.

Talking about AI systems specifically

You can answer 'how would you build this with an LLM?' like an engineer, not an enthusiast.

cost and latency as design constraints · eval strategy as an answer · failure modes · when NOT to use a model

There is no course for this one. The material is everything above: say the eval strategy out loud, name the failure modes, put a number on the latency, and know when the correct answer is a regex and a lookup table. The strongest signal you can give in an AI interview is knowing where the model does not belong.

You're done when: Asked to add AI to a product, you can describe a version that does not use a model — and say what the model buys over it.

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