I'm a software engineering student working toward AI engineering. I build AI applications end to end: LLM orchestration and RAG pipelines on the backend, cloud infrastructure, and the frontends people actually use. Most of my repos are full-stack AI systems, and that's deliberate. It's how I learn prompting, retrieval, agent design, and the production work nobody talks about.
| Layer | Technologies |
|---|---|
| LLM / Agents | LangChain, LangGraph, OpenRouter, Ollama, Prompt Engineering |
| RAG / Vector | pgvector, HNSW, text-embedding-3-small, hybrid retrieval, chunking |
| Backend | Python, FastAPI, Node.js, TypeScript, REST APIs |
| Cloud / DB | Azure (Container Apps, ACR, Key Vault, App Insights, Bicep IaC, OIDC), Supabase (Postgres + Auth + RLS), Neon Postgres, Redis, Docker |
| Frontend | Next.js, React Native (Expo), Vue, TypeScript |
| Data / ML | pandas, Jupyter, NumPy, HMM, scikit-learn workflows, InfluxDB |
Learning AI engineering by shipping real projects.
