Hari Rongali is an engineering leader with 15+ years building scalable cloud-native and ML infrastructure, currently leading ML Compute at Cruise to orchestrate 100k+ GPU training jobs across ~20k concurrent instances. He has a proven track record delivering high-performance distributed systems—from designing multi-GPU training platforms with Ray and PyTorch integrations to running production-grade CI/CD and container orchestration efforts at Intuit, MuleSoft, and VMware. Hari combines hands-on backend development (notably contributing bug fixes and enhancements to the popular Argo Rollouts CNCF project) with people leadership, having led teams that built mobile CI/CD platforms, secure certificate services, and cloud Mac farms. Comfortable across languages and environments, he repeatedly optimizes utilization and cost while improving availability for large-scale workloads. A recipient of executive training from Harvard Business School Online, he pairs technical depth with operational rigor and a knack for turning complex infrastructure problems into elegant, production-ready solutions.
5 years of coding experience
18 years of employment as a software developer
Master of Science Computer Science, Master of Science Computer Science at University of Southern California
B.Tech Electrical & Electronics, B.Tech Electrical & Electronics at National Institute of Technology Warangal
Certificate in Leadership Principles, Certificate in Leadership Principles at Harvard Business School Online
Contributions:165 reviews, 12 commits, 11 PRs in 8 months
Contributions summary:Hari primarily contributed to bug fixes and enhancements within the Argo Rollouts project, focusing on improvements to the core functionality related to canary deployments and analysis. Their work involved modifying code related to the promotion process, analysis runs, and replica count calculations during updates. The user's commits also addressed issues in the testing suite, ensuring the reliability and accuracy of the project's functionality. They also touched upon metrics and the behavior of experiment.
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