AI Engineer & Backend Developer
Computer Science student (2023–2027) building production-ready AI systems — RAG pipelines, LangGraph agents, NL-to-SQL workflows, and scalable Django backends.
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I'm a Computer Science student (2023–2027) specializing in GenAI engineering and backend development. I build production-ready AI applications that bridge modern language models with reliable, deployable systems.
I focus on RAG pipelines, LangGraph agentic systems, and NL-to-SQL workflows, with Django and FastAPI powering scalable APIs and services. My goal is to deliver practical AI that solves real problems and fits into real products.
Core stack
End-to-end Django system for natural-language querying of satellite orbital CSVs and mission PDF documents. Built with LangChain, ChromaDB, and Groq LLaMA 3.3 70B, with cosine top-4 retrieval (chunk 1,000 / overlap 200) and automated evaluation for relevance, faithfulness, and completeness.
RAG chatbot answering questions about projects and background using Groq, Django, and ChromaDB with multi-turn conversation design. Deployed to Render with a custom domain via Cloudflare DNS.
Schema-aware NL-to-SQL agent built with LangGraph, Groq LLaMA 3.3 70B, and SQLite. Includes evaluator and correction nodes, MemorySaver for multi-turn context, and conditional routing in a state machine.
Completed LLM engineering, RAG, QLoRA, and agents coursework with hands-on builds focused on production patterns and tool calling.
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