Led the development of an AI Copilot ecosystem for a healthcare client, encompassing multiple cutting-edge functionalities: • Architected and deployed an AI Copilot for healthcare with information retrieval, data analysis, and a Clinical Trial Prediction Engine using Machine Learning. • Engineered a RAG (Retrieval-Augmented Generation) system with optimized vector database for enhanced information retrieval. • Integrated GenAI technologies like RAG & Vector Databases (FAISS, Pinecone), to enhance Copilot capabilities, improving clinical trial predictions by at least 30%. • Developed an intelligent Data Analysis Chatbot capable of handling complex quantitative queries and automated data visualization. • Created a Clinical Trial Prediction Engine using Machine Learning and Deep Learning techniques to forecast trial outcomes across various stages. • Implemented a Code Interpreter for automated programming assistance and data processing. • Also managed and mentored 2 interns during this period. Single-handedly architected and deployed end-to-end data and ML pipelines on AWS and GCP, managing all aspects from data preprocessing to model deployment. This comprehensive solution streamlined clinical trial analysis, improved prediction accuracy, and significantly enhanced data-driven decision making capabilities.

Worked on medical imaging data to discern and classify various objects, contributing to the development of accurate datasets for enhanced analysis and interpretation.

Solved problems related to core Computer Science Engineering subjects.

Development of the Depot Computerization System (DCS) Application and database upgradation. Responsibilities included working on Entity-Relationship (ER) diagrams to model, structure database entities, relationships and comprehensive exploration of database structures to ensure efficient data management and retrieval.