Professional Summary

Explainability isn't a feature. It's the architecture.

A Computer Engineering graduate who builds systems that show their work — every anomaly score, risk flag, and fraud signal ships with the exact reason behind it.

Computer Engineering graduate (Savitribai Phule Pune University, Final Year SGPA 8.68, July 2026) currently building production ML systems as a Software Engineering Intern at Healthnexaa, including OCR pipelines for medical devices and the medextract library, published on PyPI. First-author researcher at ICCET 2026, presenting a 99%-accuracy crop recommendation system.

Independently designed, built, and documented 37 local AI systems spanning cybersecurity, legal tech, document intelligence, retail analytics, RAG, multi-agent orchestration, LLM evaluation, LLM infrastructure, AIOps, prompt engineering, feature stores, LLM security, federated learning, education technology, and MLOps — every one fully local, with no cloud dependency.

37
Local AI systems shipped
94–98%
OCR digit accuracy, n=144
99%
Crop model accuracy, ICCET 2026