MLDS speaker · 2026
Alok Srivastava
Head of Artificial Intelligence- Machine LearningGroww
About Alok Srivastava
Alok Srivastava is an AI leader specializing in building production-grade intelligence systems that operate reliably under real-world constraints of latency, scale, compliance, and business accountability. Currently leading AI & ML at Groww, he drives recommendations, personalization, GenAI assistants, forecasting, and decision systems that power experiences for millions of users in high-trust financial environments. With over a decade of experience, Alok has translated advanced machine learning into revenue-impacting products across consumer-scale and enterprise platforms. Prior to Groww, he worked at Amazon, contributing to large-scale ML systems where millisecond latency, fault tolerance, and rigorous experimentation were critical. His expertise spans AI-native platforms, real-time decisioning systems, human-in-the-loop and autonomous AI architectures, and ML platformization at organizational scale. In addition to his operating role, Alok advises founders and boards on AI platform strategy, production readiness, and building durable intelligence systems.
1 edition on the MLDS stage · 1 session on record

Talks at MLDS
Sessions from the MLDS agendas, newest first.
MLDS 2026
Bengaluru
26–27 Mar 2026Mar 26, 2026 · 16:50–17:10 · Hall 2 · Bengaluru
Exclusive WorkshopsFrom PoC to Production: Architecting a Financial Agent for Real-World Autonomous Decision Support
This talk walks through the real-world journey of building a production-grade Agentic Financial Assistant—from early proof-of-concept experiments to a scalable, reliable system serving real users. We will explore how large language models evolve into autonomous agents capable of reasoning, retrieving financial context, executing tools, and making safe decisions within enterprise constraints. The session will cover practical architecture patterns including multi-agent orchestration, tool execution layers, memory and retrieval systems, and LLMOps for monitoring and reliability. We will also discuss the critical production challenges—latency, cost control, hallucination mitigation, guardrails, and evaluation frameworks—and how to design agent systems that are safe enough for financial environments. Developers will walk away with practical blueprints for building agentic systems that move beyond demos into production.
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