With over 3 years of experience as a Software Engineer building AI and backend systems, I focus on developing reliable solutions that handle complex data and drive meaningful outcomes. My experience spans high-performance computing (HPC), cloud infrastructure, and Generative AI, enabling me to work across both engineering and AI domains.

Currently, I design and implement AI solutions in the healthcare domain, spanning both core AI components and applied AI systems. My work focuses on architecting multi-agent workflows and agentic retrieval augmented, developing embedding driven data pipelines, and applying strong context design to meet domain specific accuracy requirements.

I manage the end-to-end MLOps lifecycle, from data ingestion and versioning through training and deployment. The pipeline incorporates monitoring, drift detection, model evaluation, and production observability to support continuous model improvement.

At my previous startup, I designed and delivered end-to-end systems from concept through deployment, primarily building High Performance Computing (HPC) systems and Generative AI solutions for financial workflows, contributing to two core products on AWS and Azure.

Outside of work, I build full-stack solutions for clients, translating business ideas into production-ready applications. I am interested in backend and AI engineering opportunities focused on solving meaningful use cases.