Sravanthi Kondoju explains why production AI needs trustworthy data, platform engineering, governance, observability, and reliable access at scale.
About Sravanthi Kondoju
Sravanthi Kondoju is Data Platform Lead at Target, with more than 18 years of experience implementing enterprise data platforms, large-scale distributed systems, and modernized data ecosystems that support high-growth business needs. Her work focuses on platform engineering, system reliability, scalability, and operational efficiency. She speaks at global conferences about modern data platforms, AI and ML operations, and empowering women in technology, sharing practical insights with technical audiences and the next generation of women in tech.
What motivated you to join the AI Builders Global Conference?
Absolutely. I'm excited to be part of the AI Builders Global Conference because it brings together practitioners who are not only exploring AI, but also thinking about how to build and operationalize AI solutions at scale. What motivated me to join is the opportunity to share practical lessons from building and operating large-scale data and technology platforms while learning from other AI practitioners. I'm particularly interested in the intersection of data platforms, cloud technologies, AI and ML, and reliable production systems. I believe the next phase of AI innovation will depend not just on better models, but on strong platforms, trustworthy data, governance, observability, and engineering practices that allow AI systems to operate reliably at scale.
What shaped your journey through technology and AI?
I have more than 18 years of experience in technology, with a career spanning data engineering, cloud computing, data platforms, platform engineering, reliability, security, and AI and ML operations. Throughout my career, I have worked across organizations including Oracle, FedEx, ADP, Macy's, and Target, taking on increasingly complex challenges involving large-scale data and technology platforms. My journey into AI has been a natural evolution of this experience. Early in my career, my focus was primarily on data engineering and distributed data systems. As data platforms became increasingly central to analytics and business decision-making, my focus expanded toward platform architecture, reliability engineering, automation, observability, governance, and self-service capabilities. With the rapid growth of generative AI, RAG, and AI agents, I became particularly interested in the infrastructure and platform foundations required to make these technologies useful in the real world. AI applications ultimately depend on high-quality, accessible, governed data and reliable platforms. That led me to explore AI and ML operations, AIOps, intelligent automation, and agentic approaches to platform operations. I'm especially interested in how AI can be integrated into engineering workflows to improve reliability, accelerate troubleshooting, automate operational decisions, and help teams manage increasingly complex technology environments. What has inspired me throughout this journey is the opportunity to solve problems at scale, where engineering, data, cloud, and AI come together to create systems that are not only innovative, but also reliable and useful for the people who depend on them.
Why do AI-ready data platforms matter now?
The topic is important to me because I believe AI readiness begins with the data platform. Organizations often focus heavily on selecting models or building AI applications, but the effectiveness of those applications depends on the underlying data architecture. Analytics, RAG applications, and AI agents all require data that is accessible, trustworthy, governed, secure, observable, and available with the right level of freshness and context. An AI-ready data platform therefore needs to bring together several capabilities: scalable storage and compute, modern table and data formats, data governance, metadata and lineage, security, observability, self-service access, and architectures that can support both traditional analytics and emerging AI workloads. RAG and AI agents make these requirements even more important. AI systems need to retrieve the right information, understand context, interact with enterprise data and systems, and increasingly make or recommend decisions. Without strong foundations for data quality, access control, retrieval, monitoring, and reliability, AI systems can become difficult to trust and operate at scale. In my talk, I focus on architecture patterns that help organizations evolve their existing data platforms into foundations that can support analytics today while also enabling RAG and agentic AI use cases tomorrow. My goal is to connect AI innovation with practical engineering principles that organizations can actually implement and operate in production.
Who should join this conversation?
I would recommend the conference to technology leaders, data and AI engineers, architects, platform engineers, ML engineers, data scientists, and anyone responsible for moving AI initiatives from experimentation into production. It would also be valuable for engineering and product leaders who want to understand what it takes to build scalable AI capabilities rather than treating AI as an isolated application layer. For anyone working at the intersection of data, cloud, AI, platform engineering, or intelligent automation, this is an opportunity to learn from practitioners, exchange ideas, and understand the architectural and operational considerations that will shape the next generation of AI systems. I'm particularly excited about the opportunity to contribute to that conversation and share practical perspectives on building the data and platform foundations that make AI scalable, reliable, and production-ready.