Ajay Gopinathan
Senior Staff Software Engineer at Waymo
Kirkland, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolAjay Gopinathan is a Senior Staff Software Engineer in Kirkland, Washington with 13 years of experience building large-scale, production systems at Google and Waymo and a PhD in Computer Science. He blends deep academic roots in quantum information and networking with practical expertise in backend engineering, DevOps, and distributed systems. At Kubeflow Pipelines he improved build reproducibility, added advanced backend filtering, and integrated remote build execution—demonstrating a knack for making complex ML infrastructure more reliable and scalable. Comfortable shipping across the stack, he pairs algorithmic rigor (auction theory and networking) with hands-on improvements to build and CI tooling. Colleagues rely on him to translate research-grade ideas into production-ready systems that run at scale.
13 years of coding experience
14 years of employment as a software developer
PhD, Computer Science, PhD, Computer Science at The University of CalgaryBachelor of Applied Science, Computer Engineering, Bachelor of Applied Science, Computer Engineering at Nanyang Technological University Singapore
Stackoverflow
53reputation
1kreached
1answer
1question
Github Skills (22)
kubernetes10
docker10
apidoc10
dockers10
cicd10
go10
api10
kubernetes-pods10
grpc10
sql9
python8
data-pipeline8
data-pipelines8
mlops8
pipeline8
Programming languages (10)
Github contributions (5)
kubeflow/pipelines
Nov 2018 - Oct 2021
Machine Learning Pipelines for Kubeflow
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:5 releases, 184 reviews, 75 commits in 2 years 11 months
Contributions summary:Ajay contributed to the Kubeflow Pipelines project by addressing build reproducibility issues within the code generator. They implemented a fix by pinning code generator and client-go versions to a specific Kubernetes release. Furthermore, the user introduced filtering capabilities to the backend API, adding filtering for pipelines, jobs, and run servers, including a new IS_SUBSTRING operator. Lastly, the user integrated Remote Build Execution (RBE) into the project's build process, streamlining builds and enabling the API server to use Google Cloud credentials.
pipelinetektondata-sciencemachine-learningmlops
neuromage/pipelines
Nov 2018 - Oct 2021
Machine Learning Pipelines for Kubeflow
Contributions:251 pushes, 144 branches in 2 years 11 months
data-sciencemachine-learningmlopskubeflowkubernetes
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