About

Senior AI/ML Professional with over 5+ years of experience in Data Science, AI, Machine Learning, and MLOps across healthcare, automotive, and financial services industries. Currently serving as a Graduate Research Assistant at University of Maryland, College Park, conducting cutting-edge research on 7B-70B parameter LLMs and diffusion models for biomedical applications. Previously, as Senior AI/ML Engineer at Aya Healthcare, I architected production multi-agent LLM platforms processing 2.3M+ patient profiles with 95% clinical accuracy and built scalable RAG infrastructure supporting 2000+ concurrent healthcare providers across 15+ hospital networks.

At Bridgestone Group (Azuga Inc.), I led cloud operations, data architecture, and MLOps initiatives, designing infrastructure processing 24 billion sensor records (120 petabytes) with <60s latency using Apache Spark and 24-node clusters. My key achievements include developing the Accident Risk Survival Model using Cox regression (C-index: 0.78) on 7.8M crash records projecting $122.9M+ cost savings, implementing NLP sentiment analysis algorithms, and architecting a real-time streaming data warehouse with 99.8% data accuracy. Additionally, I pioneered a Generative AI-based "Chat with Data" project using LangChain and AutoGen frameworks to enhance user interactions through conversational AI.

Previously, as an Analytics Engineer at Simplilearn, I managed enterprise data pipelines, conducted advanced statistical analysis, and provided consulting services on Data Science projects for Purdue and UMass clients. My research contributions include publications on "Predictive Maintenance in Automotive using Machine Learning" (accepted for publication in IEEE Journal, February 2025) and "Optimizing Supply Chain using Data Science and AI" (published in International Journal of Applied Engineering & Technology, 2023). During my early career at Dhirtek Business Research & Consultancy, I focused on statistical modeling to predict market growth in Liquid Biopsy treatments, demonstrating early expertise in applying ML to healthcare challenges.

With a passion for AI, machine learning, and MLOps, I am committed to advancing innovations in data science and automation across diverse industries. My work spans from HIPAA-compliant healthcare AI systems to real-time fraud detection engines processing 500K+ daily transactions with 94.2% accuracy, demonstrating expertise in building scalable, production-ready AI solutions that deliver measurable business impact while maintaining the highest standards of reliability and compliance.