The harsh truth: Knowing how to write a simple Python script that calls the OpenAI API is no longer a marketable skill. You must know how to build scalable, asynchronous, production-ready AI infrastructure.
1. The Core Infrastructure
- FastAPI: Flask and Django are out for AI. FastAPI is built on Starlette, fully asynchronous, and the absolute industry standard for building high-performance AI microservices.
- Python (Asyncio): You must deeply understand async/await. If your application blocks while waiting 5 seconds for an LLM response, your server will crash under load.
- Docker & Kubernetes: AI models and complex RAG pipelines have massive dependency trees. If you can't containerize your app and deploy it on K8s, you can't work in a modern AI startup.
2. The Data Layer
- Vector Databases: Pinecone, Qdrant, or Milvus. You must know how to generate embeddings and do highly optimized similarity searches (HNSW).
- PostgreSQL (pgvector): Many startups are moving away from dedicated VectorDBs and simply using Postgres with the pgvector extension to keep structured and unstructured data together.
- Redis: Essential for Semantic Caching (saving money on duplicate queries) and managing rate limits.
3. Real-Time & Orchestration
- LangChain / LlamaIndex: Use them as glue, not as a crutch. Understand how they work under the hood.
- WebRTC / WebSockets: The future of AI is voice and real-time video. You must know how to stream data back to the client instantaneously (SSE) or handle bi-directional audio via WebRTC.