Jonathan Böcker
Ängelholms kommun, Sweden
Summary
🤩
Rockstar🎓
Top SchoolJonathan Böcker is a seasoned backend developer and DevOps engineer with 11 years of experience building cloud-native, serverless and embedded solutions across retail and hospitality domains. Based in Ängelholm, Sweden, he has delivered backend systems for IKEA and ASSA ABLOY Global Solutions, working with Go on AWS serverless, embedded Linux, and cross-platform Flutter web clients. As a consultant and hands-on engineer he blends operational rigor with development speed, improving reliability and performance in production pipelines. He also contributes to MLOps work, notably optimizing a TensorFlow fast-style-transfer video pipeline by integrating FFmpeg and batch-processing fixes for significant performance gains. His background in naval engineering and military logistics informs a pragmatic, systems-oriented approach to fault-tolerant design and tooling.
11 years of coding experience
9 years of employment as a software developer
Naval Architecture and Marine Engineering, Naval Architecture and Marine Engineering at Kalmar Maritime AcademyNon-commisioned Officer, Naval Architecture and Marine Engineering, Non-commisioned Officer, Naval Architecture and Marine Engineering at Karlskrona Naval Warfare CentreBachelor's degree, Bachelor of Science in Engineering in Computer Science, Bachelor's degree, Bachelor of Science in Engineering in Computer Science at Malmö University
Swedish, English
Github Skills (10)
neural-network10
computer-vision10
deep-learning10
tensorflow10
ffmpeg10
python10
style-transfer10
subprocess9
cloud-infrastructure5
infrastructure5
Programming languages (10)
Github contributions (5)
lengstrom/fast-style-transfer
Dec 2016 - Dec 2016
TensorFlow CNN for fast style transfer ⚡🖥🎨🖼
Role in this project:
MLOps Engineer
MLOps EngineerContributions:5 commits, 3 PRs, 5 comments in 1 day
Contributions summary:Jonathan focused on optimizing the performance of the fast style transfer application by implementing improvements to video processing. Their contributions involved integrating FFmpeg for direct piping of video frames, enhancing the batch processing mechanism, and resolving frame loss issues. These changes resulted in significant performance gains, especially when using a batch size of one, and improved the reliability of the video style transfer process.
style-transfertensorflowneural-styleneural-networksdeep-learning
Schwusch/mah-kronox-react-native
Apr 2017 - May 2017
Contributions:66 commits, 57 pushes, 2 branches in 18 days
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