Updated June 2026
AI Engineer Roadmap 2026
The complete, structured path from programming basics to production AI systems. Every stage links to a free SuperML course.
8 learning stages
12+ free courses
~1 year full-time pace
01
4–8 weeksFoundations
Skills
- Python for data science (NumPy, Pandas)
- Statistics and probability basics
- Linear algebra essentials
- Git and command line
02
6–10 weeksMachine Learning Core
Skills
- Supervised learning (regression, classification)
- Unsupervised learning (clustering, dimensionality reduction)
- Model evaluation and validation
- Feature engineering
03
8–12 weeksDeep Learning & Neural Networks
Skills
- Neural network fundamentals
- CNNs and computer vision
- RNNs and sequence models
- Transformers and attention
04
4–6 weeksLLMs & Prompt Engineering
Skills
- How large language models work
- Prompt engineering patterns
- Output format control
- Prompt evaluation and testing
05
6–8 weeksRAG & Vector Databases
Skills
- Document ingestion and chunking
- Embedding models and vector search
- Hybrid retrieval and re-ranking
- RAG evaluation with RAGAS
Free Tutorials
06
6–8 weeksAgentic AI Engineering
Skills
- Agent architectures and the ReAct loop
- Tool use and function calling
- Memory systems for agents
- Multi-agent orchestration
07
6–8 weeksMLOps & Production
Skills
- CI/CD pipelines for ML
- Model serving and Docker
- Model monitoring and drift detection
- Model registries and versioning
08
OngoingSpecialization & Certification
Skills
- LLM fine-tuning (LoRA, QLoRA)
- Enterprise AI architecture
- Claude Certified Architect prep
- Forward Deploy Engineering
Ready to start?
All courses are free and backed by real open source products you can use immediately.