Rafal Skolasinski
Senior Software Engineer at Neo4j
London, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolRafal Skolasinski is a Senior Software Engineer based in London with 11 years of experience building backend and MLOps systems, currently working at Neo4j after senior engineering and lead roles at Seldon. He combines a strong research background—a PhD in theoretical physics from Delft—with hands-on expertise deploying and hardening machine learning inference and serving platforms, contributing to notable open-source projects like Seldon Core and MLServer. Rafal’s work spans Kubernetes manifests, gRPC dataplanes, CLI tooling for batch processing, and production-focused improvements such as retries, timeouts, and logging to make model serving robust at scale. Comfortable at the intersection of research, DevOps and backend engineering, he brings a data-driven, rigorous approach to shipping reliable ML infrastructure.
11 years of coding experience
6 years of employment as a software developer
Master of Science - MS Physics, Master of Science - MS Physics at University of WarsawDoctor of Philosophy - PhD Theoretical Physics, Doctor of Philosophy - PhD Theoretical Physics at Delft University of Technology
Stackoverflow
314reputation
24kreached
2answers
2questions
Github Skills (28)
kubernetes10
deploying10
python10
machine-learning10
asyncio10
triton10
mlops10
tensorflow10
kubernetes-pods10
triton-server10
batch-processing10
grpc10
dockers9
docker9
logging9
Programming languages (27)
Github contributions (5)
SeldonIO/seldon-core
Dec 2019 - 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:2 releases, 417 reviews, 494 commits in 3 years 1 month
Contributions summary:Rafal focused on improving the example model deployments, primarily within the context of the Seldon Core MLOps framework. Their contributions included enforcing specific versions of the Tensorflow library for the MNIST example, modifying the Kubernetes manifests, and updating the training and model serving scripts. They also addressed the need to specify model details. This user demonstrated skills in deploying and managing machine learning models.
monitorservingdata-sciencedeploymentmachine-learning
SeldonIO/MLServer
Sep 2022 - Dec 2022
An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
Role in this project:Backend & DevOps Engineer
Backend & DevOps EngineerContributions:29 reviews, 20 commits, 14 PRs in 2 months
Contributions summary:Rafal primarily contributed to the backend aspects of the MLServer project, adding features for batch processing and improving the overall inference workflow. They introduced new functionality for the command-line interface (CLI) related to batch processing. The user made changes to the gRPC dataplane, incorporated a retry mechanism, and added features such as configurable connection timeouts, and request headers. Additionally, they worked on improving the logging and handling of invalid input data.
xgboostmulti-frameworkspythonseldon-corelightgbm
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