Sourabh Bajaj is a seasoned software leader and Co-Founder/CTO with 14 years of experience building distributed systems, machine learning platforms, and scalable data infrastructure. He has led engineering teams at startups and large organizations—helping prototype TPU integrations at Google, building ML and analytics platforms at Coursera, and driving personalization and platform engineering at Neeva—before founding Uplimit to modernize online upskilling. Sourabh blends hands-on backend and DevOps skills (notably contributions to the Cloud TPU reference repo and a well-used macOS dev setup) with product-minded leadership that spans hiring, platform reliability, and ML research-to-production. Based in Mountain View, he pairs academic grounding from Georgia Tech with practical open-source impact and a knack for automating developer workflows and documentation publishing.
14 years of coding experience
10 years of employment as a software developer
BITS Pilani, Birla Institute of Technology and Science
AISSCE Computer Science, AISSCE Computer Science at Sardar Patel Vidyalaya
Master of Science (MS) Computer Science, Master of Science (MS) Computer Science at Georgia Institute of Technology
Contributions:5 reviews, 237 commits, 123 PRs in 7 years 3 months
Contributions summary:Sourabh primarily contributed to the project by setting up and configuring the build and deployment process, automating tasks related to the GitBook documentation. They implemented a script for publishing the GitBook to gh-pages, automating the process of building and deploying the documentation site. Additionally, they created a script to dynamically generate a contributors list. The user also made minor changes to the documentation's appearance.
Contributions:45 commits, 43 PRs, 40 pushes in 1 year 6 months
Contributions summary:Sourabh's commits primarily involve merging internal changes into the public repository, indicating a role in integrating and releasing code. These changes include modifications to Python scripts for tasks such as image dataset handling and model configurations, which are critical aspects of the project's functionality. The changes also involve modifying configuration files and adjusting model parameters.
cloud
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