As a Generative AI Software Engineer, I design and ship scalable, production-ready AI solutions—from building multi-agent LLM orchestration pipelines using Streamlit app leveraging LangFlow and Vector databases, to contributing core features in the open-source libraries for data-science testing and visualization. I use Python, PyTorch, FastAPI, Docker/Kubernetes, and CI/CD best practices to ensure robust, maintainable code that powers real-world applications.

With a strong foundation as a Machine Learning Engineer, I’ve architected end-to-end ML workflows on both HPC clusters (SLURM) and cloud platforms like Amazon Web Services. I'm currently working on designing sparse autoencoders for particle-collision event reconstruction. I thrive on turning complex research ideas into streamlined, data-driven products.

As a Full-Stack Engineer, I’ve led the development of responsive, user-centric applications from front-end to back-end. I have built dynamic user interfaces like React.js and Tailwind CSS, design and implement REST & GraphQL APIs with Node.js/Express and Spring Boot, and manage both relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis) databases. I have containerized services with Docker, orchestrated them on Kubernetes, and automated deployments via GitHub Actions and OpenShift, securing web experiences end to end.