Gajendra Babu Thokala
Senior Engineering Leader
Seattle, Washington, United States
Actions
Gajendra Babu Thokala is a Senior Engineering Leader; architect and researcher, who has been working in the field of building large scale, real time data platforms and AI driven systems that have been utilized by hundreds of millions of people around the globe for 18 plus years. Presently, Gajendra leads important data and streaming projects at one of the largest technology companies in the world, where he develops low latency, high throughput platforms utilizing some of the most current and widely used technologies today, in addition to large scale metadata and knowledge graph systems.
Prior to his current position, Gajendra held senior leadership positions at Microsoft, where he contributed to the development of Microsoft's large-scale enterprise platforms, including Microsoft Purview, to provide large-scale data governance, privacy and compliance solutions. Gajendra's work integrates strong industrial systems engineering with applied research, specifically focused on real-time analytics, AI ready data platforms and scalable governance architectures.
Gajendra is a Senior Member of IEEE, a BCS Fellow and a Fellow of many other professional organizations. In addition, Gajendra is the author of multiple technical books and frequently reviews and serves as a Technical Program Committee (TPC) member for IEEE and Springer sponsored conferences. Additionally, Gajendra is a common speaker and session chair at international conferences.
Links
Area of Expertise
Topics
Real-Time Data Platforms as the Foundation for Intelligent Systems
Discussions around intelligent systems tend to center on AI models and algorithms, but that’s not always where problems start. Problems often start much earlier, when data arrives late, lacks completeness, can’t be trusted, or is missing critical context. Long before intelligence shows up in an application, it is shaped by the data platform underneath it.
This talk takes a step back and looks at intelligent systems from a practical angle: the data platforms that enable them. Many systems still rely on batch pipelines, which made sense when decisions could wait. That approach no longer works for many modern use cases. Especially in today’s AI era, getting data faster matters more than ever. This talk explores how streaming, event-driven architectures help systems react in real time rather than hours later. It explains how stateful processing and scalable pipelines keep data flowing continuously, instead of relying on snapshots that quickly fall out of date.
6th International Conference On Computational Intelligence (ICCI2025)
From the Field - Migration of Enterprise LOB Applications (Java Platform) to Office 365 using CAM
Understand the approach for choosing an App Model for Enterprise Applications.
Discuss about Single App Vs Multiple Apps using Azure web role.
Discuss about the implementing multiple business use cases with single app(strategy pattern).
Scaling a provider hosted App with distributed cache.
Microsoft TechReady19
The Data Platform Imperative for AI
As AI becomes embedded in real-world products and operational systems, their limitations are increasingly shaped by the infrastructure responsible for supplying data. Many AI initiatives in the Industry today struggle to deliver timely and reliable outcomes because the underlying platforms were never built for continuous, high-fidelity data movement.
This keynote explores the engineering foundations required to support intelligence at scale. It looks at how architectural decisions around data ingestion, processing, and coordination directly affect system responsiveness, reliability, and the quality of downstream decisions. By contrasting legacy, periodic processing approaches with modern, continuously operating architectures, the talk shows how platforms designed for constant change enable faster adaptation and more consistent behavior in AI-enabled systems.
Rather than concentrating on model capabilities, the discussion emphasizes the engineering required to build data platforms that remain effective as information evolves. The keynote outlines how event-driven, scalable architectures transform delayed insights into continuously updated intelligence, with practical guidance for teams shaping future AI-enabled platforms.
3rd World Congress on Smart Computing
(WCSC2026)
Responsible AI as an Engineering Discipline: Bridging Experimentation and Deployment
The gap between the number of experimental AI initiatives and the number deployed at scale across industries and governments is growing exponentially. As this gap grows, so does the risk of biased algorithms, opaque decision-making processes, violations of consumers' right to privacy, misuse of AI technology, and the continued erosion of public trust in AI technologies' ability to function safely, fairly, and reliably.
This session views Responsible AI as an engineering profession with specific practices and operational rigor versus as aspirational policy or a checklist of regulatory compliance. The discussion will focus on how safety, trust, inclusiveness and accountability must be incorporated throughout each stage of the AI life cycle (data, training models, deploying production, operational management) to build scalable responsible AI technologies that maintain public trust and deliver sustained societal benefit.
INDIAai Pre-Summit 2026
Scaling AI Responsibly: Governance, Safety, and Real-World Deployment
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.
Intelligence Breaks Before the Model: Why Real-Time Data Matters
Most intelligent systems don’t fail because of bad models. They fail because the data arrives late, lacks context, or can’t be trusted enough. Long before AI system response shows up in an application, its limits are already set by the data platform underneath.
This talk looks at intelligent systems from that starting point. Rather than putting all the attention on models, this talk looks at how real-time, event-driven platforms ultimately shape what AI can and cannot do. Talk discusses why batch pipelines fall short for modern use cases and how streaming, stateful architectures allow systems to react as events happen, not hours later. When time and state are treated as first-class concerns, real-time features and low-latency decisions become possible by design.
The talk also highlights the role of “Context” and how Metadata and knowledge graphs help systems understand where data comes from, how it changes, and how pieces fit together. That context makes systems not just faster, but easier to trust and explain.
If you’re building or operating intelligent systems, this session offers a practical view of what really matters before AI ever enters the picture.
https://aiccons.com/keynote_speakers/mr-gajendra-babu-thokala/
Feeding the Agents: Real-Time Data Infrastructure as the AI Moat
In 2026, the way enterprises compete has changed. With most foundations able to utilize a commodity model, the ability to differentiate lies in what feeds the model: the enterprise data infrastructure. Autonomous agents don't fail because they can't reason. They fail because their environment, the context, is old, inadequate, or unreliable.
This lecture by Gajendra Babu Thokala, a long-term industry professional, illustrates how real-time data infrastructures have become the strategic moat for all production AI. Using his almost twenty years of experience leading engineers in some of the world's largest tech firms, including building a real-time data system that serves over 100 million users, this speaker will outline the four-layer reference architecture that powers modern autonomous systems, how to get from batch to streaming economics, and what it takes to run mission-critical AI workloads at scale.
He'll explore actual use case examples from personalized content, fraud detection, supply chain intelligence, and content discovery, backed up by large-scale metrics for latency, throughput, and business impact. When he leaves, attendees will understand where moats are currently being built today, why governance and lineage are becoming boardroom issues, and what engineering capabilities the next generation of practitioners will need for the next ten years.
Industry Expert Lecture as part of our "Industry Specialized Lecture Series," organized by BIMA
Gajendra Babu Thokala
Senior Engineering Leader
Seattle, Washington, United States
Links
Actions
Please note that Sessionize is not responsible for the accuracy or validity of the data provided by speakers. If you suspect this profile to be fake or spam, please let us know.
Jump to top