Final-year AI + Chemical Engineering dual major at IIT Gandhinagar, building production-grade software — RAG platforms, semantic search, real-time collaboration tools, and test automation. Previously ASCENT intern at Axis Bank (Automation for TCoE).
The short version: I like building software that holds up in production.
I'm a senior undergraduate pursuing a dual major in Artificial Intelligence and Chemical Engineering at IIT Gandhinagar, focused on software development. I enjoy designing backend systems, retrieval pipelines, and developer tooling that are fast, reliable, and measurable.
As an ASCENT intern at Axis Bank, I built a Java + Selenium test recorder with a self-healing replay pipeline that cut manual test authoring from hours to under two minutes. My recent work includes a multi-tenant RAG platform with FastAPI and PostgreSQL, a semantic product search engine, and a real-time collaborative code editor — working across Python, Java, C++, JavaScript, and SQL.
IIT Gandhinagar
CPI: 8.00 / 10
Dr. V.G.I. Paranjape, Rahimatpur
88.33%
Podar International School, Satara
95.20%
Selected projects from coursework, competitions, and research. Live on GitHub.
AI research platform for document ingestion, evidence retrieval, grounded answers, and web fallback. Multi-tenant RAG on FastAPI with PostgreSQL RLS and Google OAuth 2.0; HNSW + BM25 hybrid retrieval with RRF lifted Recall@5 from 74% to 91% on MS MARCO / SciFact. CRAG with Groq / Tavily behind Nginx load balancing cut P95 latency 30%+ at 450+ req/s.
Semantic search over 12,732 Flipkart products using all-MiniLM-L12-v2 embeddings in FAISS (with Prof. Anirban Dasgupta). Four retrieval models spanning graph traversal, metadata re-ranking, and LLM query expansion, benchmarked via a Flask API — 0.897 NDCG on semantic queries.
Collaborative online code editor with real-time multi-user editing over WebSockets — room-based sessions, live code sync, syntax highlighting, and session state management. Frontend on Vercel, backend on Render.
Inter-IIT Tech Meet 13.0 (IIT Bombay). Sequential Genetic Algorithm with a Deepest-Bottom-Left heuristic for ULD packing, using Modified OX, 2-OPT / orientation mutation, and constraint validation. Converged in ~70 generations to a total cost of 32,936 — within 1,336 of optimal — at 78.86% mean volumetric efficiency.
Paper notes, project write-ups, and the occasional rant.
What the 2017 paper actually says, why it worked, and what surprised me on a careful re-read in 2026.
read post →How fusing dense and sparse retrieval took Recall@5 from 74% to 91% — and what it cost in latency.
in draft →Notes from the FedEx cargo-packing project — modelling tricks that worked and what I'd do differently.
in draft →Open to software engineering and AI / ML engineering roles. Always happy to chat.