Ravi RS Nadimpalli

Ravi RS Nadimpalli

Growth PM

AWONE
Ravi RS Nadimpalli brings a one-of-a-kind blend of product leadership, public policy innovation, and hands-on startup, enterprise product (Growth) experience. He has been in AI space for 4+ years now, and adapted to Vibe coding through LLMs, he experiments with Lovable, Bolt, Cursor every week and calls himself, "Vibe Coder with Product Sense"
 
With over a decade of work across Government, startups, and global enterprises like NTT Data and BYJU's FutureSchool, Ravi is known for getting his hands dirty building, scaling, and transforming systems from the ground up. Having built Product & Ecosystem Initiatives for Government of India, Ravi is on a mission to monetize India’s digital public infrastructure. He also serves as a Growth PM at AWONE, helping scale AI and data solutions across industry sectors. From re-architecting legacy systems using microservices to securing funding from Meta for immersive education initiatives, Ravi’s track record is full of high-impact projects. His work has spanned EdTech, GovTech, Cyber Security, eCommerce, and public policy—making him a powerhouse of practical insights for anyone aspiring to work in today's evolving tech and policy landscapes. Ravi is passionate about vocationalizing higher education, gamifying entrepreneurship, and bridging institutional gaps through ethical, tech-enabled design. Fun Fact: He once failed 13 SSB interviews and still made a thriving career by reinventing himself at every stage. His philosophy? "Perfection is the enemy of progress."

What happens when developers hand off part of the heavy lifting to AI? In the Vibe Coding Showdown, three panelists-from different technical backgrounds-set out to solve the same ambitious app challenge using AI-powered coding assistants. The result? Three applications, each built with a mix of human intent and machine-generated code.

This session walks you through how they did it-how AI helped brainstorm, build, debug, and refine complex apps using just natural language, iterative feedback, and smart tooling. Whether you’re a developer or just AI-curious, you’ll see how AI is shifting the way we approach software creation.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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