AI Conclave Bengaluru

Welcome Note

• Sandeep Dutta, President, India and South Asia, AWS

Keynote

• Ganapathy (G2) Krishnamoorthy, VP, Databases, AWS
• Bhavnish Lathia, Chief Technology Officer, Kotak Mahindra Bank
• Jaiprakash, Vice President - Technology, Zomato
• Srikanth Ramasubramanian, Head - Enterprise AI, Axis Bank

Closing Keynote

• Biswajit Das, Head of Data & AI, AWS India
• Dinesh Karthik, former Indian Cricketer, RCB mentorand Coach
• Dr. Paul Hawkins, Mathemetician and Inventor of Hawk-eye sports technology
• Joy Bhattacharjya, Sports Producer and Analyst

The role of data in agentic AI

Everyone knows you need good data for effective AI agents and applications, but what does that truly mean? In this session, discover the role your most valuable data plays in agentic AI applications. Learn how Model Context Protocol (MCP) allows you to access your data wherever it is, whether you use it for Retrieval Augmented Generation (RAG), or use agents to operate on data. Understand how agentic AI data access patterns map to modern data management practices. Discover best practice architectures using AWS database services like Amazon Aurora and OpenSearch Service, along with analytical, data processing and streaming experiences found in Amazon SageMaker and Amazon S3. Learn data lake, governance, and data quality concepts and how Amazon Bedrock AgentCore and Bedrock Knowledge Bases, and other features tie an end-to-end agentic AI solution together.

• Siva Raghupathy, Director, Worldwide Database & Analytics Solutions Architecture, AWS
• Prudhvi Vasa, Head of Data, Postman

AI Agents from POC to production with Amazon Bedrock AgentCore

Going from a pilot to a secure, scalable, and reliable agent system requires more than great prompts. In session, we'll show how Amazon Bedrock AgentCore helps teams move from proof-of-concept to production using enterprise-grade services, including Runtime, Gateway, Identity, Memory, Observability, Code Interpreter, Browser, Policy and Evaluations . Learn how these services simplify development, reduce operational burden, and improve time-to-value.

• Vatsal Shah, Data & AI Solutions Architecture Lead, AWS India
• Nilav Ghosh, Senior Director, AI Center of Excellence, Innovacer

Master AI model development with Amazon SageMaker AI

Unlock the complete AI development lifecycle with Amazon SageMaker AI's comprehensive platform. This technical session explores how SageMaker AI provides an integrated end-to-end solution for building, training, deploying, and managing AI models at scale—supporting every stage from initial experimentation to production operations. Dive into key features like serverless model customization, end to end development with HyperPod, efficient model inference options, and flexible development environments. Join this session to learn on how SageMaker AI accelerates AI innovation while optimizing resource utilization.

• Rama Thamman, Senior Manager, AI Platforms, AWS
• Anshuman Mahapatra, VP, Senior AI Architect, EXL

Building enterprise-grade GenAI Platform on AWS

As enterprises scale GenAI from pilots to production, building secure, reusable platforms is essential. This technical deep-dive reveals how to architect enterprise-grade Generative AI on AWS using proven patterns and reference architectures. Explore model access, prompt orchestration, RAG, fine-tuning, and agentic workflows with Amazon Bedrock, SageMaker AI, and Bedrock AgentCore. Address critical platform concerns: multi-account strategies, access controls, data isolation, observability, CI/CD pipelines, and guardrails. Learn how leading enterprises combine AWS services with open-source tools to accelerate deployment, enforce governance, and deliver AI-as-a-Service internally—while maintaining cost control and measurable ROI for sustainable AI innovation at scale.

• Alessandro Cerè, Principal Generative AI Solutions Architect, AWS
• Hitesh Sharma, Senior Director, Software Engineering, AIML & GenAI, Aris Global

The next frontier: Building the agentic future

Explore the transformative future of AI Agents with AWS's groundbreaking Nova ecosystem. Discover Nova Forge's revolutionary "open training" approach enabling organizations to build proprietary models infused with their unique data, and Nova Act's breakthrough reliability in browser-based automation. Learn how three new frontier agents—Kiro autonomous developer, Security Agent consultant, and DevOps operational specialist—work independently for extended periods as seamless extensions of your teams. See how AWS Transform dramatically accelerates legacy application modernization, eliminating massive maintenance costs. Explore simplified model customization through Reinforcement Fine Tuning in Amazon Bedrock and serverless capabilities in SageMaker AI.

• Praveen Jayakumar, Technology Head, Generative & Agentic AI, AWS

A leaders guide to agentic AI

In a landscape of rapid AI evolution and daily headlines, senior leaders need clarity on what Agentic AI means for their business. This session cuts through the noise to provide a practical understanding of autonomous AI agents and their strategic implications for your organization. We will demystify Agentic AI and focus on what matters most to leaders. We will share how both management and architecture models will need to evolve to get value out of the autonomous AI systems.

• Arvind Mathur, Executive in-Residence, Enterprise Strategy, AWS
• Ankit Mandholiya, Head Equity and Derivatives, Motilal Oswal

Scaling generative AI and agentic AI use-cases across industries

Discover how leading organizations are using GenAI and Agentic AI to solve complex business challenges. This session showcases real-world GenAI implementations across industries demonstrating proven frameworks for scaling POCs into production-ready solutions that drive measurable business outcomes.

• Suraj Dutt, APJ Tech Solutions GTM Lead, AWS
• Nishant Kejriwal, GM - Product & Technology, Novus Hi-Tech Robotics

Transform business intelligence with AI-powered analytics

Discover how AI transforms traditional business intelligence through integrated analytics. Learn implementation strategies for combining machine learning, natural language processing, and automated insights. Explore architectural frameworks that enable real-time, adaptive BI systems responding to business needs. See case studies demonstrating how organizations achieve faster, more accurate decisions through comprehensive AI-powered data analysis.

• Michael Armentano, Principal Worldwide GTM Specialist, Agentic AI, AWS
• Akansha Kumari, Head of Product, PocketFM

Responsible AI: Building systems people can trust

Building AI that people can trust means thinking about safety, fairness, and transparency from day one - not as an afterthought. Organizations that succeed with AI use proven frameworks to guide their decisions, asking important questions like: Is our AI treating everyone fairly? Can we explain how it makes decisions? What happens if something goes wrong? For AI agents that work independently, this becomes even more important. Smart companies build in checkpoints where humans review key decisions, set clear boundaries for what AI can and cannot do, and continuously monitor performance. Companies like Indeed show how this approach works in practice—they've built systems that protect millions of job seekers while still moving fast and innovating. The key is making responsible AI part of your culture, not just a compliance checklist.

• Sahil Verma, Generative AI Specialist Solutions Architect, AWS India
• Shiv Brat Pandey, Senior Director, Delhivery

Balance cost, performance & reliability for AI at enterprise scale

Deploying generative AI at enterprise scale requires balancing performance, cost, and reliability across diverse business purposes and use cases. Amazon Bedrock offers a complete portfolio of inference options, with on-demand cross-region inference for elastic scaling, on-demand service tiers for balancing performance and cost, including optimization options like prompt caching for improving latency while significantly reducing cost, and batch inference for cost-effective bulk processing. This interactive session covers the tools and approaches needed to architect hybrid inference strategies that enable enterprises to maximize price-performance ratios as AI workloads scale.

• Sudhanshu Hate, Principal GenAI/ML Solutions Architect, AWS India
• Anshu Avinash, Head of Artificial Intelligence, DevRev

Build modern applications with Amazon Aurora DSQL

As organizations modernize applications for scalability and agility, database architecture becomes crucial. This session explores Amazon Aurora DSQL, a serverless, distributed SQL database for next-generation workloads. Learn to build modern serverless applications using Amazon Aurora DSQL with Amazon API Gateway and AWS Lambda. Examine performance characteristics, scaling behavior, and cost considerations, along with current limitations. Gain insights on choosing Amazon Aurora DSQL for modernization efforts, comparing it to alternatives like Amazon DynamoDB and Amazon Aurora Serverless v2.

• Nanda Kaushik, Director, Software Development, Aurora & Opensearch, AWS

Best practices for building Apache Iceberg based lakehouse architectures on AWS

Discover advanced strategies for implementing Apache Iceberg on AWS, focusing on Amazon S3 Tables and integration of Iceberg Rest Catalog with the lakehouse in Amazon SageMaker. We'll cover performance optimization techniques for Amazon Athena and Amazon Redshift queries, real-time processing using Apache Spark, and integration with Amazon EMR, AWS Glue, and Trino. Explore practical implementations of zero-ETL, change data capture (CDC) patterns, and medallion architecture. Gain hands-on expertise in implementing enterprise-grade lakehouse solutions with Iceberg on AWS.

• Kiran Guduguntla, Principal Worldwide Specialist, 3P Analytics, AWS
• Amir Shenavandeh, Senior Worldwide Specialist Solutions Architect, Data, AWS

Deep dive into model serving engines on AWS

Deploying Large Language Models effectively requires navigating complex choices across inference frameworks and engines—decisions that can impact model performance. This comprehensive session delivers a data-driven evaluation of leading LLM serving frameworks including vLLM, TensorRT-LLM, TGI, SGLang, SageMaker AI LMI, and Dynamo with a deep dive analysis of the performance characteristics, and deployment trade offs. We start with inference fundamentals - KV caching, batching strategies, and quantization - then dive into head-to-head framework comparisons measuring latency, throughput, and cost efficiency. You'll gain practical expertise deploying these frameworks on both Amazon SageMaker and Amazon EKS, with production-ready patterns for auto-scaling, monitoring, and cost optimization

• Ashwini Kumar, Senior EC2 Specialist Solutions Architect, AWS India
• Vijay Narayanan, Technology Head, Kotak Mahindra Bank

Spec-driven development: Shaping the next generation of AI software

When you are working through a complex feature you need to make tradeoffs between requirements, system design, and implementations details before you deal with the nitty-gritty of writing the code. In this session, learn how spec-driven development can help you go from idea to production without sacrificing any of the crucial development details.

• Chandrasekaran Sivaraman, Senior Solutions Architect, Developer Specialist, AWS India
• Krishna Chaitanya, Head - Data & ML, CommerceIQ

Oracle Database@AWS 

Oracle Database@AWS is a multicloud offering resulting from a strategic partnership between Oracle and Amazon Web Services (AWS) that allows customers to run Oracle Database services on Oracle Cloud Infrastructure (OCI) deployed within AWS data centers. This unique architecture provides high performance and low-latency connectivity between the Oracle databases and applications running on AWS

• Manish Punjabi, Head, Worldwide Specialists, Commercial Databases, AWS
• Sandeep Mukherjee, Director - Data Cloud Engg, Oracle India

Welcome note: Data & AI built on AWS for India

• Luke Anderson, Director, Data & AI GTM, APJ, AWS

Unlocking business value with a modern data platform – J&K Bank Case Study

• JaiPrakash Dave, Partner Solutions Architect, AWS India
• Mohammad Muzaffar Wani, Chief Information Officer, Jammu & Kashmir Bank

Building intelligent operations: How Swiggy leverages agentic AI to enhance app availability and dev velocity ‎

• Gaurav Gupta, Principal Solutions Architect, AWS India
• Swapnil Mehta, Senior Software Development Engineer, Swiggy

From data foundation to Agentic AI - Freshworks' journey at scale

• Sriram Sundarajan, Principal Solutions Architect, AWS India
• Sreedhar Gade, VP - Engineering, Freshworks

Merchant QR to intelligent payments at scale on AWS

• Punit Jain, Senior Solutions Architect, AWS India
• Gagan Kumar Verma, CTO, Paytm Payments

The agentic lender

• Ayush Agrawal. Solutions Architect, AWS India
• Tarang Gupta, Head of Analytics & AI, DMI Finance

Turning emerging online sellers into power brands through a data driven operating system

• Gaurav Malhotra, Senior Solutions Architect, AWS India
• Yaswanth Kothainti, VP - Engineering, Razor Group

Architecting the future of sports entertainment: SportsCraft on AWS

• Anirudh Chawla, Analytics Specialist Solutions Architect, AWS India
• Biswajit Biswas, Chief Data Scientist, Tata Elxsi