About
In 2025, I completed an internship at Jio in Navi Mumbai, one of India's largest telecommunications and technology conglomerates, where I gained hands-on industry experience. I also won a hackathon in the ML domain at Universal AI University, competing against teams across the region. I am currently pursuing a B.Tech in Computer Science & Engineering (AI & ML) at LTCOE, Mumbai — recognized as the college topper in my 1st and 2nd year.
Work Experience
Skills
Check out my latest work
I've worked on a variety of projects, from simple websites to complex web applications. Here are a few of my favorites.
With the growing Indian startup ecosystem and no single place to track funding activity, I built a dashboard that aggregates and visualizes startup funding rounds from 2024 onwards. It enables fuzzy search, filtering, charts, and leaderboards — making funding data easy to explore and analyze.
This project builds an end-to-end banking analytics solution to analyze customer data, loans, and deposit trends. It transforms raw financial datasets into structured insights using Python, SQL, and Power BI. The goal is to support data-driven decision-making through interactive visualizations and business intelligence.
This local RAG system uses LangChain, Qdrant, and Ollama to transform natural language into precise SQL queries while maintaining total data privacy. By leveraging a vector store for automated schema context, it allows users to seamlessly query complex databases using plain English without external API keys.
This Medical Chatbot uses Retrieval-Augmented Generation (RAG) to provide factual, context-aware answers by grounding a TinyLlama model in a trusted medical knowledge base. Built with LangChain, Pinecone, and Flask, it is fully containerized and ready for automated deployment on AWS via GitHub Actions.
I like building things
During my time in university, I attended 1+ hackathons. People from around the country would come together and build incredible things in 2-3 days. It was eye-opening to see the endless possibilities brought to life by a group of motivated and passionate individuals.
UAI Hawkathon 2026
Mumabi, Maharashtra
We built an AI-based off-road semantic scene segmentation system that can understand complex environments using synthetic data. The solution used a Vision Transformer model for accurate feature extraction and segmentation. We also developed a real-time interface to visualize predictions live. The project was designed as an end-to-end pipeline including training, evaluation, and deployment. It achieved ~79.4% pixel accuracy and 0.412 IoU, with strong focus on real-world applicability and generalization.
Get in Touch
Want to chat? Just shoot me a dm with a direct question on twitter and I'll respond whenever I can. I will ignore all soliciting.