Title of talk: Scaling AI Responsibly: Governance, Safety, and Real-World Deployment

Abstract: 

Responsible AI represents the intersection of innovation and regulation, where AI technology is rapidly evolving; however, the gap between experiments and safe, deployable systems is growing wider. Organizations are accelerating their use of AI and risks associated with biased algorithmic decision-making, lack of transparency surrounding those decisions, privacy violations and misuse of AI technologies are all beginning to emerge. These issues have the potential to be detrimental to both the long-term success of AI and the regulatory compliance organizations face.

Instead of viewing Responsible AI as an aspirational policy document or a list of compliance items to check off, we will view it as a true engineering discipline requiring specific, operational, and system-level design and practice. To create a trustworthy AI system, you must include the concepts of safety, transparency, fairness, and accountability throughout the AI lifecycle including data collection, model training, deployment, monitoring, and continuous improvement.

Using real-world examples of production systems, this presentation will demonstrate how engineering teams can operationalize responsible AI in the development and delivery of large-scale platforms. This includes incorporating governance mechanisms, risk controls, and reliability practices into AI architecture and workflows. By recognizing Responsible AI as a fundamental systems engineering problem, organizations can transition away from experimentation toward developing scalable AI systems that will support public trust and provide meaningful economic and social benefits.

Profile: 

Gajendra Babu Thokala is a senior engineering leader and distributed systems architect specializing in large-scale data platforms, artificial intelligence, and real-time streaming systems. With over 18 years of experience in the technology industry, he has led the design and development of global-scale data and AI platforms that power mission-critical products used by hundreds of millions of users.

He currently works as an Engineering Leader and Architect for large-scale real-time data platforms at a top-tier technology company in the United States, where he focuses on building high-performance distributed systems using technologies such as Apache Flink, Kafka, Spark, and NoSQL databases. His work centers on creating intelligent data infrastructures that support knowledge graphs, metadata intelligence, and responsible AI systems at scale.

Previously, he spent more than a decade at Microsoft, where he contributed to the development of Azure Purview, Microsoft's data governance and compliance platform. His work helped build capabilities for metadata discovery, lineage, and privacy governance across billions of enterprise data assets, supporting regulatory frameworks such as GDPR and CCPA.

Gajendra is an active contributor to the global technology community. He is an IEEE Senior Member and holds fellow memberships in multiple professional organizations. He regularly serves as a reviewer and technical program committee member for international conferences and mentors engineers and early-career professionals. His research and thought leadership focus on real-time data platforms, responsible AI systems, data governance, and knowledge graph architectures.

He is also an author and speaker who shares insights on engineering large-scale intelligent systems and the evolving intersection of AI, data governance, and distributed computing.