AI Engineering Skills
Definition
AI Engineering skills include Python programming, LLM API integration, RAG system design, prompt engineering, vector databases, AI agent development, evaluation methods, and production deployment practices.
Core Technical Skills
Must-Have Skills:
- Python programming and async patterns
- LLM API integration (OpenAI, Anthropic, etc.)
- Prompt engineering and optimization
- RAG system implementation
- Vector databases (Pinecone, Weaviate, Chroma)
- Basic understanding of LLM fundamentals
Advanced Skills:
- AI agent development with LangChain/LangGraph
- Fine-tuning and model customization
- Evaluation and testing frameworks
- LLMOps and observability (LangSmith, Langfuse)
- Production deployment and scaling
Soft Skills
Beyond technical abilities, successful AI Engineers need: ability to translate business requirements into AI solutions, strong debugging and problem-solving skills, communication skills to explain AI capabilities and limitations, and judgment about when AI is (and isn’t) the right solution.
Learning Priority
Start with Python and LLM APIs, then build progressively: basic prompting → RAG systems → agents → evaluation → production deployment. Focus on building projects that demonstrate these skills rather than just completing courses.