Python foundations
Idiomatic Python — types, comprehensions, generators, dataclasses, async. Enough fluency that you stop fighting the language and start expressing ideas in it.
Math & statistics
Just enough linear algebra, calculus, and probability to read papers and reason about model behavior. We skip what you don't need.
Data manipulation
NumPy, pandas, Polars. Slicing, joining, reshaping, plotting. Skills that pay rent every single day in this field.
Classical machine learning
Linear/logistic regression, trees, ensembles, clustering. Build the intuitions that make every later stage make sense — and learn what to reach for when an LLM is the wrong hammer.
Deep learning
PyTorch from autograd up. Build CNNs and RNNs from scratch before you ever import a pretrained model. The mechanics are the moat.
NLP & transformers
Tokenization, embeddings, attention, the transformer block. Implement a tiny transformer from scratch — the rest of the field will feel inevitable afterwards.
LLM fundamentals
How modern LLMs are pretrained, what RLHF actually does, why context length matters, and the operational shape of an inference call.
Prompting & evaluation
Structured prompting, output formats, and — more importantly — building eval harnesses that catch regressions before users do.
RAG systems
Embeddings, vector stores, chunking, hybrid retrieval, reranking, citation extraction. Build one end-to-end and break it deliberately.
Fine-tuning
When fine-tuning helps and when it doesn't. LoRA, QLoRA, full SFT, preference optimization. Run a real fine-tune on a real dataset.
Agents & tool-use
Function calling, planner/executor loops, MCP, multi-step reasoning, and the failure modes that ship most agent demos straight to the graveyard.
Deployment & serving
vLLM, batching, quantization, autoscaling, cost models. Make an LLM-powered service that doesn't fall over at 10x traffic.
Observability & safety
Traces, structured logs, online evals, prompt injection defense, output filtering, and the on-call playbook for an AI system.
Capstone — ship it
Build, deploy, evaluate, and write up an end-to-end AI system of your choosing. Open-source it. Get one practitioner review. This is the artifact you put on the top of your résumé.
Trek complete. What's next?
You've walked the full roadmap. Now ship the capstone, write about it, and share the path with the next engineer who needs it.