Deepak Bysani

I've spent twenty years building the systems enterprises bet their business on. Now I teach the engineering discipline around them.

At Visa, as Chief Systems Architect, I led the platform re-architecture of one of the world's largest merchant payment processing systems — moving from monolith to microservices using domain-driven design, event-driven architecture, and bespoke orchestration layers. I designed the real-time fraud detection engine that replaced a legacy product, improving catch rates at payment-network latency. I was responsible for product and solution architecture integrating Asia's biggest banks.

At Akamai, over seven years, I grew from Solutions Architect to Senior Architect for Distributed Data Engineering — building real-time stream processing systems and big data solutions for Akamai's streaming and IoT platforms. I managed engineering teams, established development practices, and built tools for managing one of the world's largest distributed networks.

At Nutanix, as Senior Staff Software Engineer, I help shape the future of multi-cloud management — driving engineering for Nutanix Cloud Manager across automation, cost governance, AI-driven operations, and security orchestration for 15,000+ enterprises.

Along the way: a fintech rewards platform at Zeta, AI-assisted low-code automation at Jiffy.ai, decision-science analytics at Mu Sigma, and enterprise systems for JPMorgan Chase.

Why training

The pattern repeated at every company I joined. Teams adopted AI tools. Individual output rose. Delivery didn't move. The bottleneck relocated — from writing code to reviewing, testing, and trusting it.

Then a second problem arrived. AI entered the product itself. Confidently wrong answers with perfectly green traces. Conventional testing and monitoring couldn't see the failure.

I'd spent my career building safety systems around complex software — fraud detection, distributed orchestration, resilience engineering. The disciplines that make AI-assisted code safe to ship are the same disciplines that make AI features verifiable in production. They just hadn't been taught as engineering practice.

That's what I do now.