Ravi Kiran Pagidi
Senior AI Data Engineer at Navy Federal Credit Union
Chantilly, Virginia, United States
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Ravi Kiran Pagidi is a Senior Data Engineer and AI/Data Systems Researcher with over 11 years of experience designing and delivering scalable data platforms, analytics ecosystems, and AI-enabled enterprise solutions. His expertise spans data engineering, big data, data analytics, machine learning, generative AI, agentic AI, and cloud-native data architectures. Across his career, he has focused on building reliable, production-ready systems that transform complex data into actionable intelligence and measurable business value.
In addition to his industry work, Ravi is actively engaged in the global research community. He has authored 2 books, published 20+ peer-reviewed research papers, and contributed 20+ peer reviews for international conference research papers. His research and professional interests center on production-ready AI systems, intelligent automation, modern data platforms, and the practical application of advanced analytics to real-world problems.
Ravi is especially passionate about bridging the gap between research and enterprise implementation, helping organizations move beyond experimentation toward trustworthy, scalable, and impactful AI and data solutions. His talks combine technical depth with practical perspective, making complex concepts accessible to both engineering and broader technology audiences.
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Building Production-Ready Generative AI Systems: From Data Foundations to Enterprise Deployment
Generative AI has moved quickly from experimentation to enterprise adoption, but many teams still struggle to move beyond demos into scalable, reliable, and governed production systems. This talk will explore what it really takes to build production-ready Generative AI solutions in enterprise environments, with a focus on the data and platform foundations that make these systems successful. It will cover how data engineering, data quality, retrieval architecture, governance, observability, and cloud-native design influence the performance and trustworthiness of AI systems. Attendees will gain a practical understanding of the architectural patterns, risks, and design decisions involved in deploying Generative AI responsibly at scale.
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