Ai Engineer

This is for building production LLM applications when you need more than a quick API call. It covers the full stack: RAG pipelines with vector databases like Pinecone and Qdrant, agent orchestration with LangGraph and CrewAI, multimodal AI integration, and the operational stuff that matters in production like prompt injection prevention, cost controls, and semantic caching. Use it when you're architecting AI features that need to scale, not prototyping demos. The behavioral emphasis on reliability over proof-of-concept is honest about what enterprise AI actually requires. It assumes you have real data sources and deployment infrastructure, so skip it for quick UI tweaks or traditional ML work.