Siddhant Bapusaheb Tanpure

Title of the Talk:
Engineering Trustworthy AI Infrastructure at Hyperscale

Abstract:
The rapid expansion of AI-driven applications has introduced new challenges in cybersecurity, privacy, and cloud-scale infrastructure management. Organizations must now build intelligent systems that not only deliver high performance but also comply with evolving global security and data protection requirements.

This talk examines the design of secure and privacy-first AI systems within modern cloud computing environments. Key topics include distributed AI architectures, zero-trust infrastructure principles, secure data processing pipelines, privacy-preserving machine learning integration, and operational safeguards for large-scale AI platforms. The session also discusses emerging trends in AI infrastructure security, resilient cloud engineering, and responsible deployment strategies for intelligent systems.

Attendees will gain practical insights into how scalable AI ecosystems can be engineered to support both innovation and trustworthy computing in increasingly complex digital environments.

Bio:
Senior Software Engineer with 10+ years of experience building distributed AI infrastructure, machine learning systems, and hyperscale cloud platforms at Meta and Microsoft. Specializes in AI safety infrastructure, large-scale ranking systems, privacy-aware computing, and resilient cloud architectures supporting billions of daily events. Contributor to PyTorch open-source development, active peer reviewer across IEEE/Elsevier venues, and Distinguished Fellow of the Soft Computing Research Society.