I will develop ai agents, rag systems, ai saas web application, ai software developer

If your AI feature works in a demo. It breaks in production. Real users, concurrent requests, sensitive data that is where demo-grade builds fail.

I design and deploy production grade RAG chatbots and AI agent for startups and engineering teams that need their AI to work at scale.

My builds combine LangGraph orchestration, FastAPI backends, vector database retrieval (Qdrant, pgvector, Pinecone, LanceDB) and AWS for deployment with Docker, Kubernetes, and Terraform depending on your scale into systems that are containerized, tested, and cloud-deployed not cobbled together and handed off.

Every system includes hybrid search with reranking for accurate RAG results, structured outputs, rate limiting, and observability

I Deliver:

1. RAG pipeline: Document ingestion, Chunking, Hybrid retrieval, reranking, LLM generation
2. AI agent: LangGraph-based agent with planning, multi-step reasoning, tool calling, memory, human-in-the-loop where needed
3. Backend API: FastAPI, async, auth, rate limiting, Pydantic-validated schemas
4. Deployment: AWS cloud deployment with full infrastructure-as-code CI/CD pipeline and deployment documentation
5. Observability: LLM trace logging to see model performance