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AI Engineer Roadmap
Definition
An AI Engineer roadmap is a structured learning path that guides developers from foundational skills through advanced AI engineering topics like RAG, agents, and production deployment.
Recommended Learning Path
Phase 1: Foundations (4-6 weeks)
- Python proficiency (async, type hints, virtual environments)
- LLM fundamentals (tokens, context windows, temperature)
- First LLM API integrations (OpenAI, Anthropic)
- Basic prompt engineering
Phase 2: Core Skills (6-8 weeks)
- RAG systems (embeddings, vector databases, chunking)
- Prompt optimization and evaluation
- Building simple chatbots and assistants
- Working with structured outputs
Phase 3: Advanced Topics (8-12 weeks)
- AI agents and multi-agent systems
- LangChain/LangGraph frameworks
- Fine-tuning basics
- Production deployment and monitoring
Phase 4: Specialization (Ongoing)
- Choose a focus area (agents, RAG, voice AI, etc.)
- Build portfolio projects
- Contribute to open-source
- Stay current with rapid changes
Key Success Factors
Build projects at each phase rather than just studying. Focus on depth over breadth - it’s better to deeply understand RAG than to superficially know 10 different topics. Join communities to learn from practitioners and stay updated on the fast-moving field.