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Learning path

LLM Engineer

Specialize in LLM inference, fine-tuning, serving, quantization and optimization.

L3 · AdvancedtoL6 · Doctoral4 courses~11 h

What you’ll be able to do

  • Master LLM internals: attention, sampling, KV cache and context management.
  • Build retrieval and agent systems on top of LLMs.
  • Size inference memory, serving and batching for real deployments.
  • Relate transformer behaviour back to deep-learning fundamentals.

Your course sequence

Work through these courses in order. Each lesson links straight into the reader.

  1. 1
    LLM FoundationsIntermediate~2 h

    How large language models turn text into tokens, attention, and predictions.

  2. 2
    Applied LLM Systems: RAG, Agents & MCPAdvanced~3 h

    Build systems around a model: retrieve knowledge, call tools, and run agent loops.

  3. 3
    AI Systems Engineering: Run, Operate & EvaluateExpert~3 h

    Make a model fit, serve it fast, keep it working, and prove it improved.

  4. 4
    Deep Learning & the FrontierExpert~3 h

    From how networks learn to where AI is going.