Tomasz Dudek
Head Of AI at Chaos Gears
Warsaw, Masovian Voivodeship, Poland
Summary
👤
Senior🎓
Top SchoolTomasz Dudek is a Head of AI with a decade of hands-on experience building ML systems, data platforms and cloud-native production solutions across manufacturing, healthcare, retail and finance. He founded and scaled the Data & AI practice at Chaos Gears from 0 to a multi-disciplinary team and now oversees production-grade agents, voice bots, intelligent document processing and modern data architectures. Technically fluent in functional programming, AWS and Spark workflows, Tomasz has contributed to notable open-source tooling like sparkmagic to bridge Jupyter and remote Spark clusters. He combines practical engineering (Java/Scala/Kotlin roots) with DevOps and ML expertise, and brings a fast-learning, pragmatic approach to turning prototypes into reliable services. Based in Warsaw, he’s an AWS Machine Learning Hero who quietly champions Clojure for data science while juggling strategy, customer delivery and the occasional AWS Console.
10 years of coding experience
7 years of employment as a software developer
Math and Stats, Math and Stats at Wrocław University of Science and TechnologyComputer Science, Computer Science at University of Wroclaw
Polish, English, German
Github Skills (11)
pandas10
pyspark10
spark10
python10
jupyter10
magick10
magicleap10
r9
jupyter-notebook8
scala8
sql-query6
Programming languages (14)
Github contributions (5)
jupyter-incubator/sparkmagic
Oct 2017 - Feb 2018
Jupyter magics and kernels for working with remote Spark clusters
Role in this project:
Back-end Developer
Back-end DeveloperContributions:41 commits, 1 PR, 24 comments in 4 months
Contributions summary:Tomasz primarily worked on enhancing the `sparkmagic` project, which involves Jupyter magics and kernels for interacting with remote Spark clusters. Their contributions focused on implementing the `%%send_to_spark` magic, enabling the transfer of local data (strings and Pandas DataFrames) to the Spark context. They also made adjustments to support different Spark kernel types (Scala, Python, and R) and added features to control the amount of data sent. Furthermore, they updated documentation and fixed associated test cases.
kernelclustersjupyter-notebookmagickernels
tomaszdudek7/airflow_project
Feb 2019 - Nov 2019
scaffold of Apache Airflow executing Docker containers
Contributions:82 commits, 12 PRs, 19 pushes in 8 months
containersscaffolddockerapacheapache-airflow
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