AI/ML engineer building agentic RAG systems — hybrid retrieval, graph reasoning, and LangGraph pipelines, shipped as real deployed products, not notebooks.
AI/ML Engineering student at Government Engineering College Rajkot, focused on production LLM systems — hybrid retrieval RAG, graph-based RAG, and agentic pipelines built with LangGraph.
I've shipped two full-stack AI applications end to end — backend to deployment — and I'm currently building an agentic RAG system combining vector, keyword, and graph retrieval behind a quantitative evaluation layer.
Before this: a Flutter Developer internship building real-time infrastructure with Firebase — where the full-stack instinct started.
A production agentic RAG system combining hybrid search (Pinecone dense + BM25 sparse) with graph-based retrieval (NetworkX) and live web search (Tavily) for multi-source Q&A.
An 8-node LangGraph pipeline that turns a vague project idea into a downloadable PDF blueprint — market validation, system architecture, tech stack, and a phase-wise build plan.
interrupt() — pauses mid-execution for clarification, resumes after the user answers.Hybrid RAG chatbot combining dense HuggingFace embeddings with BM25 sparse vectors on Pinecone, using parent-child semantic chunking (~390 indexed chunks) for PDF and YouTube transcript Q&A. Full session isolation with conversation memory in Supabase.