I specialize in Generative AI, Agentic AI, and Retrieval-Augmented Generation (RAG) — developing AI agents that combine reasoning with real-world context. My work often involves orchestrating LLMs using LangChain and LangGraph, backed by vector databases and scalable cloud deployment pipelines.
I ❤️ transforming abstract AI ideas into working systems — whether it’s creating context-aware RAG chatbots, autonomous research agents, or end-to-end ML workflows. With hands-on experience in AWS, Docker, Flask, and GitHub Actions, I ensure every project I build is scalable, reproducible, and deployment-ready.
My technical interests span LLM orchestration, MLOps/LLMOps, NLP, and Computer Vision, grounded in a solid understanding of statistics, linear algebra, and probability. I thrive at the intersection of data, reasoning, and production engineering — turning experimental AI into reliable systems.
Beyond development, I’m an active community contributor — serving as a core member of JUET JDSC and former GDSC Coordinator, where I’ve led workshops and hackathons on emerging AI technologies and open-source innovation.
🚀 Currently exploring opportunities in Generative AI, LLM Applications, and Cloud-based AI Systems — always eager to collaborate, innovate, and build something truly intelligent.
⦿ Follow me on Kaggle 🌐: Kaggle
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⦿ Shoot Me an Email 💌: utkarshyadav72a@gmail.com