Damian Kalinowski
Deep Learning Software Engineer at Intel Corporation
Pomeranian Voivodeship, Poland
Summary
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Rockstar🎓
Top SchoolDamian Kalinowski is a Deep Learning Software Engineer with six years of experience building and deploying performant ML systems at Intel in the Pomeranian Voivodeship, Poland. He specializes in backend and MLOps work for inference-serving, having directly integrated OpenVINO into a scalable model server to support async requests, dynamic batching and model versioning with shape-aware handling. His background includes hands-on validation engineering, giving him a strong foundation in reliability and test-driven deployment. Damian combines practical performance tuning with production-aware model management, enabling efficient inference at scale. He holds a computer science degree from Uniwersytet Gdański and brings a pragmatic, systems-oriented approach to taking research models into robust production services. An understated strength is his focus on precise telemetry and timing utilities, which improves observability for complex inference pipelines.
6 years of coding experience
1 year of employment as a software developer
Licencjat (Lic.), Computer Science, Licencjat (Lic.), Computer Science at Uniwersytet Gdański
Github Skills (12)
openvino10
grpc9
apidoc9
cprogramming-language9
c-language9
api9
machine-learning8
kubernetes-pods5
dockers5
python5
kubernetes5
docker5
Programming languages (6)
Github contributions (5)
openvinotoolkit/model_server
Mar 2020 - Jan 2023
A scalable inference server for models optimized with OpenVINO™
Role in this project:
Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:4 releases, 3647 reviews, 421 commits in 2 years 10 months
Contributions summary:Damian's contributions focused on compiling the server with OpenVINO, a scalable inference server for models. They integrated OpenVINO for async inference requests and implemented time measurement utilities. Moreover, they introduced and utilized dynamic batching parameters, as well as handled model versioning, including specific shape configurations, to ensure proper functioning of the models being served. Their work demonstrates skills in model deployment and performance tuning.
pytorchscalableservingdeep-learningdag
dkalinowski/openvino.genai
Jun 2024 - Mar 2025
Contributions:53 pushes, 22 branches in 9 months
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