Sharan Babu Paramasivam Murugesan

Engineering Intelligence: Designing Scalable AI Architectures for the Digital World

Abstract:

As artificial intelligence moves from isolated models to embedded infrastructure, the challenge is no longer simply building accurate systems, but engineering scalable AI architectures that are reliable, adaptive, and ready for real-world deployment. This keynote explores how intelligence can be designed as a systems capability across modern digital platforms, combining data pipelines, foundation models, agents, observability, automation, and governance into cohesive architectures that operate at scale. The session will examine the architectural principles required to move from experimentation to production, including modular design, telemetry-driven feedback loops, resilience patterns, human oversight, and platform-level integration. It will also discuss how enterprises can build AI systems that are not only powerful, but also operationally sustainable, explainable, and aligned with the demands of a rapidly evolving digital world.  

Profile:

Sharan Babu Paramasivam Murugesan is a Senior Software Engineer at Microsoft with over a decade of experience in cloud computing, intelligent systems architecture, and applied machine learning. He specializes in building scalable, resilient cloud-native platforms and advancing AI-driven automation for complex enterprise systems. His work focuses on the intersection of artificial intelligence, observability, and large-scale digital infrastructure, with an emphasis on making modern systems more adaptive, reliable, and operationally intelligent.

At Microsoft, Sharan contributes to Azure’s core AIOps platform, where he develops near real-time diagnostic systems and semantic correlation engines that leverage AI agents to interpret, reason over, and act on massive volumes of telemetry data. His work supports faster incident detection, deeper diagnosis, and more intelligent operational response across cloud-scale environments. By combining large-scale systems engineering with practical AI innovation, he is helping shape the next generation of observability solutions for modern digital infrastructure.

His broader interests include AI-native operations, intelligent automation, distributed systems resilience, and the design of scalable architectures that bring together data, models, and operational decision-making in production environments.