Buddh Prakash
Senior Software Engineer at Google
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolBuddh Prakash is a Senior Software Engineer based in San Francisco with 11 years of experience building scalable infrastructure and data integration systems at Google and Palantir. He has deep expertise in deployment infrastructure, cloud data syncing, and Kubernetes node-level resource management, having contributed production features to Foundry and introduced cgroup hierarchy support in Kubelet during an early Google internship. At Google he progressed from intern to senior engineer, shipping large-scale services, and his open-source contributions include improving scikit-learn’s cross_val_predict to handle sparse predictions more robustly. He combines strong systems-level instincts with data-focused engineering, comfortable moving between low-level runtime concerns and higher-level data pipelines. His background includes a dual B.Tech/M.Tech in Computer Science from IIT Kharagpur, reflecting a solid academic foundation behind his pragmatic, production-first approach.
11 years of coding experience
5 years of employment as a software developer
Dual Degree ( B.Tech + M.Tech ), Computer Science and Engineering, Dual Degree ( B.Tech + M.Tech ), Computer Science and Engineering at Indian Institute of Technology, Kharagpur
English, Hindi
Github Skills (10)
data-analysis10
scikit-learn10
machine-learning10
python10
scikit10
cross-validation9
testing9
sparse-matrix9
numpy8
data-science8
Programming languages (4)
Github contributions (5)
scikit-learn/scikit-learn
Aug 2015 - Aug 2015
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:7 commits, 4 PRs, 30 comments in 14 days
Contributions summary:Buddh contributed to the `scikit-learn` project by modifying the `cross_val_predict` function. Their work included adding checks for sparse predictions, optimizing the concatenation of prediction blocks, and reordering predictions using inverted locations. Additionally, the user added a test case to specifically verify the behavior of `cross_val_predict` with sparse prediction inputs, improving the reliability of the library.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
dubstack/kubernetes
May 2016 - Sep 2016
Contributions:2 PRs, 169 pushes, 38 branches in 3 months
gcpcontainer-clusterdockerkubernetescluster-manager
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.