Simba Khadder is the founder and CEO of Featureform, now at Redis, working on real-time feature orchestration and building a context engine for AI and agents.

Context Engineering 2.0, Simba Khadder // MLOps Podcast #352

Join the Community:

https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

Feature stores aren’t dead — they were just misunderstood. Simba Khadder argues the real bottleneck in agents isn’t models, it’s context, and why Redis is quietly turning into an AI data platform. Context engineering matters more than clever prompt hacks.

// Bio

Simba Khadder leads Redis Context Engine and Redis Featureform, building both the feature and context layer for production AI agents and ML models. He joined Redis via the acquisition of Featureform, where he was Founder & CEO. At Redis, he continues to lead the feature store product as well as spearhead Context Engine to deliver a unified, navigable interface connecting documents, databases, events, and live APIs for real-time, reliable agent workflows. He also loves to surf, go sailing with his wife, and hang out with his dog Chupacabra.

// Related Links

Website: featureform.comhttps://marketing.redis.io/blog/real-time-structured-data-for-ai-agents-featureform-is-joining-redis/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Simba on LinkedIn: /simba-k/

Timestamps:

[00:00 ] Context engineering explanation

[00:25 ] MLOps and feature stores

[03:36 ] Selling a company experience

[06:34 ] Redis feature store evolution

[12:42 ] Embedding hub

[20:42 ] Human vs agent semantics

[26:41 ] Enrich MCP data flow

[29:55 ] Data understanding and embeddings

[35:18 ] Search and context tools

[39:45 ] MCP explained without hype

[45:15 ] Wrap up