I am a passionate Full-Stack AI Developer studying B.Tech in Artificial Intelligence & Machine Learning at Rishihood University (2024–2028). I specialize in shipping production-grade AI systems, RAG pipelines, and vector databases at scale.

Currently building: AI-powered veterinary tools as AI/ML Intern @ Vetifi.

Currently learning: Distributed systems design & agentic AI workflows.

"I turn 'it works on my machine' into 'it works in production'."

• 🥈 Runner-Up — IIIT Delhi Hackathon
• 🥈 2nd Place — IIT Roorkee Development Competition
• 🌱 Open Source — 6 merged pull requests
• ⚔️ Competitive Programming — Pupil at Codeforces, 200+ questions solved

• Developing AI-powered veterinary solutions using LLMs, RAG pipelines, and modern AI frameworks.
• Contributing to backend development with FastAPI, Python, and vector databases.
• Building and optimizing AI workflows for information retrieval and domain-specific Q&A.
• Collaborating with the founding team on production-grade AI systems and agentic workflows.

An AI-powered advisory platform that fuses LLMs, RAG pipelines, and live government data APIs into context-aware guidance for smallholder farmers.

• 🤖 Semantic RAG: Chat using Groq (Llama 3) + Pinecone for low-latency, grounded answers.
• 🌿 Disease Diagnosis: Engine mapping symptoms to expert-aligned treatment.
• 📊 Unified Dashboard: Aggregating mandi prices, weather, and government schemes.
• 🏗️ Modular Architecture: Built on FastAPI + React + Qdrant/Pinecone.

An AI legal assistant for Indian courts enabling natural-language case search, automated drafting, and Q&A across thousands of judgments.

• 📚 Citation-Grounded: RAG pipeline over Qdrant ensuring zero hallucinated outputs.
• ✍️ Automated Drafting: Generating legal drafts from 1,800+ templates in minutes.
• 🔀 Dual Retrieval: Groq (cloud) + Ollama (local/offline) with seamless mode switching.
• 🏗️ Service Layer: Scalable architecture design for easy expansion to new legal domains.