AI Engineer roadmap
Everything worth learning to build and operate production LLM systems, ordered so each layer holds up the next. Tick things off as you learn them - progress is saved.
14 stages375 topics
Essential, don't skip Recommended, skip only with a reason Optional, awareness is enough Course on PracticAI
1
Programming Foundations
2
Data Handling
3
Machine Learning Basics
4
Deep Learning Basics
5
NLP for Applications
6
Transformers
7
LLM Engineering
8
RAG
9
Agents and Tool Use
10
Backend Engineering
11
Data Stores
12
Evaluation and Observability
13
Deployment and Operations
14
Professional Practice
Capstone
One production-style AI knowledge assistant, containing:
- Document ingestion pipeline
- Hybrid retrieval with reranking
- Grounded answers with citations
- Tool calling with human approval
- FastAPI backend with auth
- PostgreSQL and vector storage
- Redis caching
- Background processing
- Tests with mocked providers
- Retrieval and LLM evaluation
- Traces, latency and cost monitoring
- Docker and cloud deployment
Finish with one strong end-to-end capstone and two smaller projects, not a dozen abandoned tutorials.
Anyone can wire up a model call. The engineers who get hired are the ones whose systems keep working when the model returns garbage, the vector store is slow, and someone pastes a prompt injection into a support ticket.