I build production-grade LLM apps and agentic systems (Agno, LangChain, RAG) with strong MLOps on AWS/GCP.

• 🧠 Personal AI Assistant (full-stack; MCP/LangGraph/Agno) that connects to email, calendar, and files
• ⚙️ Production templates: FastAPI + async workers, observability, and K8s deploys
• 📚 Sharing notes on scaling RAG (caching, retrieval, evals)

• ML/LLM architecture & agentic workflows
• RAG performance (latency ↓, quality ↑), evals, and guardrails
• MLOps: CI/CD, containers, cloud, monitoring

Languages: Python · TypeScript
AI/LLM: Agno · LangChain · PyTorch · Transformers
Backend: FastAPI · gRPC · Async IO
Data/IR: PostgreSQL · MongoDB · Elastic · Neo4j
MLOps/Cloud: Kubernetes · Docker · Terraform · AWS/GCP · Triton · ONNX

• 🚀 Personal AI Assistant — FastAPI + LangGraph + MCP + EC2 deploy
• 📎 RAG Starter — Elastic / PG vector, evaluators, caching, guardrails (WIP)

• ✉️ Collaborations on production LLM systems & infra
• 🧩 Consulting: RAG performance, evals, and platformization