The rapid evolution of Generative AI is reshaping industries, yet real-world deployment demands more than just powerful models—it requires trust, scalability, and domain alignment. This talk explores how we can architect and operationalize trustworthy generative AI systems across enterprise and healthcare environments, from cloud to edge. Drawing on 15+ years of experience in AI leadership, I will unpack practical frameworks for deploying transformer-based models, federated learning architectures, and privacy-preserving GenAI applications that align with regulatory and ethical imperatives.

Attendees will gain insights into designing secure, explainable AI systems that address real-world challenges such as electronic health record summarization, predictive diagnostics, claims optimization, and enterprise document intelligence. The session will also highlight strategies for achieving model robustness, transparency, and policy compliance—key pillars for responsible AI adoption. Whether you’re scaling GenAI in production or exploring edge-AI opportunities, this session offers a blueprint for building AI that is not only powerful—but purposeful.