Srikanth Kavuri
Engineering Trust in AI Systems: Quality-First Architectures for Intelligent Cloud and Data Platforms
Abstract:
Artificial Intelligence systems are increasingly embedded in mission-critical platforms such as healthcare, insurance, and large-scale cloud ecosystems. While AI promises automation and intelligence, its real-world success depends heavily on the quality, reliability, and governance of the underlying software and data pipelines. This keynote explores how quality engineering must evolve as a first-class architectural discipline to support trustworthy AI systems at scale.
Drawing from over a decade of hands-on experience delivering enterprise AI-enabled platforms within regulated environments, this talk presents a quality-first framework for designing, validating, and operating intelligent cloud and data systems. The session examines how AI disrupts traditional testing models, why legacy QA approaches fail in modern AI workflows, and how automation, data integrity validation, and risk-based quality strategies can be embedded directly into system architecture.
Attendees will gain practical insights into validating AI-driven decision systems, ensuring data consistency across cloud ETL pipelines, mitigating bias and data drift, and building resilient automation frameworks that support continuous delivery. The keynote offers a practitioner-led perspective on aligning AI innovation with accountability, reliability, and long-term system trust.
Profile:
Srikanth Kavuri is a senior technology professional with over a decade of experience in AI-enabled quality engineering, cloud data platforms, and enterprise automation for mission-critical systems. He has led the design and validation of intelligent platforms supporting healthcare, insurance, and public-sector programs, where system reliability, data integrity, and regulatory compliance are essential.
His expertise centers on establishing quality engineering as a core architectural discipline for AI-driven systems. Srikanth has developed automation-first validation frameworks, cloud ETL quality controls, and risk-based testing strategies that ensure trustworthy AI outcomes in highly regulated environments. His work addresses challenges such as data drift, model validation, pipeline resilience, and continuous delivery at scale.
Beyond industry practice, he serves as a peer reviewer for international journals and IEEE conferences and contributes to scholarly and professional publications. Through keynote speaking and technical leadership, Srikanth advances responsible AI adoption by aligning innovation with accountability, reliability, and long-term system trust.