Garrett Rodrigues
Technical Program Manager at Google
Boston, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolGarrett Rodrigues is a Technical Program Manager with 11 years of experience bridging machine learning, cloud infrastructure, and product delivery, currently driving content delivery and Kubernetes efforts on Google Cloud. He combines hands-on engineering roots from startup roles and MLOps contributions—such as deploying model-serving services on GKE and integrating ML pipelines into the Kubernetes ecosystem—with program-level coordination across product, legal, and marketing. His MS in Computer Science (AI/ML) from UCSD informs a practitioner’s approach to problems like model operationalization and scalable deployment. Based in Boston, he’s known for taking features from conception through execution at Google while retaining a developer’s curiosity about tooling and reproducible ML workflows. An interesting thread through his career is the blend of low-level systems work (early hardware and embedded rotations) with modern cloud-native and ML operational concerns, giving him a rare end-to-end perspective.
11 years of coding experience
3 years of employment as a software developer
University of California San DiegoBachelor of Science (BS), Electrical and Computer Engineering, Bachelor of Science (BS), Electrical and Computer Engineering at Franklin W. Olin College of Engineering
English, French
Github Skills (21)
kubernetes10
python10
flask-ask10
kubernetes-pods10
flask10
scikit9
machine-learning9
scikit-learn9
docker8
rest-api8
dockers8
api-rest8
restful-api8
api-design8
github-ci7
Programming languages (7)
Github contributions (5)
kubernetes-retired/contrib
Jul 2016 - Jan 2017
[EOL] This is a place for various components in the Kubernetes ecosystem that aren't part of the Kubernetes core.
Role in this project:
MLOps Engineer
MLOps EngineerContributions:42 commits, 54 PRs, 11 pushes in 5 months
Contributions summary:Garrett primarily worked on integrating machine learning models into the Kubernetes ecosystem. They developed a Flask-based web application for issue label prediction, deployed it on Google Kubernetes Engine (GKE), and integrated it with the existing codebase. The user's contributions included creating deployment scripts, defining persistent volumes for model storage, and updating the model through API endpoints, indicating a focus on model serving and operationalization. They also modified existing components to interact with this new ML service.
placeeolk8skubernetesecosystem
grodrigues3/community
Sep 2016 - Aug 2019
Kubernetes community content
Contributions:31 pushes, 13 branches in 2 years 10 months
k3dkubernetes-communityremote-developmentnamespacesk8s
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