Technical AI Leader · Principal Generative AI Solutions Architect · AWS
Expert on scaling autonomous AI agents safely & efficiently, AI governance, and strategic leadership.
With 20+ years of experience, Mani builds Generative AI strategy for enterprise customers at AWS. Her current focus is scaling autonomous AI agents safely and efficiently, from building AI platforms from scratch to governing agentic systems at scale. Researcher, author, and speaker sharing insights in the Agentic Enterprise newsletter.
Mani Khanuja is a Technical AI Leader and Principal Generative AI Solutions Architect at AWS with 20+ years of experience building AI platforms from scratch and driving enterprise AI strategy. She works directly with customers to build their Generative AI strategy, from architecture to production deployment at scale.
Her current focus is scaling autonomous AI agents safely and efficiently: developing stateful, memory-driven agents with personalization, advancing AI governance frameworks, and translating cutting-edge research into real-world enterprise systems. She is the author of Applied Machine Learning and High-Performance Computing on AWS and co-author of the upcoming The AI Steering Wheel.
A pioneering researcher, she co-authored the widely cited paper "Keyword search is all you need: Achieving RAG-level performance without vector databases using agentic tool use." She is also a recognized technical speaker at Re:Invent, Grace Hopper Celebration, AI Engineer Summit, and AWS Summits worldwide. She resides in Seal Beach, California, where she stays active with long runs along the coast.
Deep-dives on Agentic AI, RAG, Amazon Bedrock, distributed training, and more, top videos by views.
Insights on Agentic AI, ethics, enterprise deployment, and the future of autonomous systems, from the Agentic Enterprise newsletter.
Technical deep-dives published on the AWS Machine Learning Blog.
From high-performance computing to the agentic frontier, bridging theory and enterprise practice.
A comprehensive guide to building, training, and deploying ML models at scale on AWS. Covers distributed training, feature stores, SageMaker, and production ML pipelines for enterprise workloads. Co-authored with Farooq Sabir, Shreyas Subramanian & Trenton Potgieter.
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Unified Framework for Scaling Generative and Agentic Systems
Three interlocking layers, Strategy, Operations, and Engineering, keep AI products aligned from the first decision through the last deployment. Co-authored with Dr. Fouad Bousetouane.
Peer-reviewed papers advancing retrieval-augmented generation, agentic systems, and applied machine learning.
Keynotes, deep-dives, and technical sessions at leading industry events worldwide.
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