I am a Data Scientist and GenAI Engineer specializing in building production-grade AI systems that drive measurable business impact. With nearly five years of enterprise experience across healthcare, finance, e-commerce, and technology, I design scalable machine learning and generative AI solutions that move beyond experimentation into real-world deployment. My expertise spans Generative AI, Agentic RAG architectures, LLM orchestration, predictive modeling, cloud-native data engineering, and MLOps. I have built multi-agent reasoning systems, clinical risk prediction engines, and large-scale analytics platforms processing millions of records daily across AWS, Azure, and GCP environments. I operate across the full AI lifecycle — data ingestion, feature engineering, model development, evaluation, deployment, monitoring, and governance. My work emphasizes explainability, performance optimization, cost efficiency, and secure deployment in regulated environments. I bring strong experience in model interpretability, drift monitoring, experiment tracking, and automated retraining pipelines. What differentiates me is the ability to combine deep technical execution with strategic thinking. I collaborate effectively with product, engineering, DevSecOps, and executive stakeholders to deliver AI systems that are scalable, auditable, and aligned with enterprise objectives. Currently pursuing a Master’s in Data Science (AI & ML), I remain focused on advancing enterprise AI capabilities in Generative AI, intelligent automation, and responsible AI systems. Open to opportunities in Senior Data Scientist, Applied AI, and GenAI Engineering roles where impact, ownership, and innovation intersect.
