Abhimanyu Selvan

I've spent my career in rooms where "trust me" doesn't cut it because the stakes were too high.

Building safety-critical cockpit systems for Boeing 787 flight simulators at Lockheed Martin, where bugs could affect pilot training. Leading automated guided vehicle systems at Oceaneering, where precision errors could shut down factory floors. Architecting medical IoT platforms at Onera Health, where system failures could endanger patients. Shipping Otomi (now the Akamai App Platform) at Red Kubes, where a broken Kubernetes deploy cascades through every customer. You don't earn credibility in those environments through promises. You earn it by proving your system actually works.

That's the mindset I carried into AI systems engineering. The systems get bigger, the stakes get more abstract: uptime, latency, error budgets, failure modes. The principle is the same. Prove it works before you claim it does.

The output across those roles is what makes the case. At Onera I built an internal developer platform on Kubernetes that took deployment frequency from twice a week to daily. At Red Kubes I cut deploy time on Otomi from twenty seven minutes to eleven. At DigitalOcean I led the developer advocacy team globally. The work behind that role: a developer portal prototype that compressed time-to-first-deploy from fifteen minutes to three, and partner integrations with AMD, LangChain, and Glitch that drove seven hundred and fifty thousand in ARR. The tactics scale with the stage. The principle does not. Build the thing, instrument it, ship it, and let the numbers say whether it worked.