Alex Rakowski
Senior Member Of Technical Staff at AMD
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAlex Rakowski is a Senior Member of Technical Staff based in San Jose with nine years of experience turning advanced research into production-grade systems that make large language models run faster, cheaper, and more efficiently on GPU clusters. With a PhD in condensed matter physics and a multi-year postdoc background at Berkeley Lab and UC Irvine, he blends deep scientific rigor with practical MLOps and systems design at AMD. He contributes to open-source MLOps tooling—improving Seldon Core's deployment and metrics robustness—showing attention to operational detail beyond model research. Alex favors simplicity and elegant code, aiming for lightweight, well-connected systems that scale in real environments. Colleagues rely on him for bridging the gap between high-performance GPU infrastructure and reliable, production ML workflows. An understated strength is his track record of refactoring complex systems to improve metric consistency and operational resilience.
9 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Condensed matter physics, Doctor of Philosophy (Ph.D.), Condensed matter physics at The University of ManchesterA Levels, Maths, Chemistry, Physics, Economics, A Levels, Maths, Chemistry, Physics, Economics at Charters School
Github Skills (9)
kubernetes10
kafka10
machine-learning10
deploying10
kubernetes-pods10
cicd9
go9
python8
yaml7
Programming languages (8)
Github contributions (5)
SeldonIO/seldon-core
Oct 2021 - Jan 2023
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Role in this project:
MLOps Engineer
MLOps EngineerContributions:207 reviews, 76 commits, 54 PRs in 1 year 3 months
Contributions summary:Alex's contributions focused on improving the Seldon Core MLOps framework. They fixed a typo in the admission webhook, preventing issues with TensorFlow protocol handling. They disabled the scraping of explainer metrics and added support for progress deadlines in Seldon deployments, enhancing operational capabilities. Further improvements involved refactoring Kafka configuration handling and optimizing the executor's worker ID mechanism for better metric consistency.
monitorservingdata-sciencedeploymentmachine-learning
agrski/greg
Jun 2021 - Dec 2023
Golang tool to search for arbitrary strings in GitHub orgs/repos
Contributions:5 reviews, 47 PRs, 128 pushes in 2 years 6 months
golanggithub-botstringsorgsgolang-tool
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