Amazon

I build data platforms at Amazon Prime Video that handle marketing measurement and analytics worldwide — 400+ dataset types, dozens of source systems, billions of rows. Most of my recent work has been figuring out how to deploy AI agents into production without them making things worse. We run six specialized agents for automated data investigation and Ive spent the last year learning what happens when you give AI systems real responsibility over real business decisions.Before Amazon I spent six years at Macys leading enterprise forecasting and replenishment systems. Im an IEEE Senior Member, Ive published research on silent data corruption in AI-augmented pipelines at IEEE ICDCS and ISDFS, and I gave the keynote at IEEE NEXOTECH 2026 on data quality in the age of AI. I review papers for ICDM, VLDB, ACM RecSys, DSN, and about a dozen other venues. I also do editorial reviews for CRC Press.When Im not building things, Im usually trying to figure out why something that worked yesterday broke today — which is basically the problem my agents are supposed to solve.

Samsung Semiconductor

I am a Staff AI/ML Software Engineer at Samsung Semiconductor, where I work on enterprise AI systems spanning retrieval-augmented generation, agentic AI, document intelligence, and AI-powered knowledge automation.My work focuses on taking generative AI beyond prototypes into production. I have architected and technically led enterprise RAG platforms, multi-agent workflows, permission-aware knowledge agents, deep-research systems, and integrations with enterprise platforms such as Jira and Confluence. Since joining Samsung, I have architected or technically led more than 10 AI solutions adopted across multiple business functions, with a strong focus on secure access to enterprise knowledge, grounded outputs, observability, evaluation, and human oversight.I am also a published AI researcher. My work includes Patentformer, published at the EMNLP 2024 Industry Track, and PatentVision, published at the EACL 2026 Industry Track. I am also a co-inventor on a U.S. patent application related to AI technology.I am an IEEE Senior Member and have served as a Technical Program Committee member and reviewer for multiple IEEE conferences. My areas of interest include enterprise AI agents, retrieval systems, responsible AI, agent evaluation, and production architectures for trustworthy autonomous systems.

Walmart

Suresh Kumar Gunasekaran is a Senior Software Engineer at Walmart Global Tech with more than 16 years of experience designing and delivering large-scale enterprise technology solutions. His expertise spans artificial intelligence, agentic AI, cloud-native architecture, distributed systems, financial platforms, retail technology, and enterprise automation.Throughout his career, Suresh has worked on complex technology platforms across retail, financial services, and enterprise environments, with a focus on building scalable, resilient, and intelligent systems that solve real-world business problems. His current interests include autonomous AI agents, generative AI, enterprise knowledge systems, AI governance, intelligent decision automation, and the integration of AI into mission-critical enterprise workflows.Suresh is also actively involved in the broader technology and research community through technical publications, peer review, conference participation, and technology judging. His published research explores practical applications of Agentic AI and Vision-Language Models in retail operations, including autonomous replenishment decision-making and intelligent shelf-health monitoring.He has reviewed numerous research papers for international technology conferences and participates in technology and innovation judging programs. He is a Fellow of the Institution of Electronics and Telecommunication Engineers (IETE), a Fellow of the Soft Computing Research Society (SCRS), and is involved with professional engineering and technology organizations including IEEE and the Robotics Society.Suresh is passionate about translating emerging AI research into reliable, governed, production-ready systems. His speaking interests focus on practical lessons from building enterprise AI platforms, designing trustworthy autonomous agents, scaling AI within cloud-native environments, and preparing organizations for the transition from AI assistants to autonomous enterprise systems.