Bogdan Girman
Machine Learning Engineer at idealo internet GmbH
Berlin, Germany
Summary
👤
Senior🎓
Top SchoolBogdan Girman is a Berlin-based Machine Learning Engineer with 14 years of experience building reliable backend and infrastructure systems, recently transitioning from senior system and DevOps roles into ML at idealo. He has a strong track record automating delivery, monitoring and testing pipelines, and has led architecture and production-support teams across startups and enterprise projects. Notable open-source contributions include extending moto to better mock AWS SageMaker and CloudWatch—improving tests for ML workflows by adding Experiments, Trials, Feature Groups and pagination. Bogdan combines hands-on Python and cloud experience with leadership in process automation and a pragmatic focus on stability and observability. Colleagues describe him as fast-learning, persistent and organized, able to take sole charge or integrate smoothly into teams.
14 years of coding experience
18 years of employment as a software developer
Computer Engineer Computer Science, Computer Engineer Computer Science at Ukrainian State Chemical Technology University, DnipropetrovskThe expert of economics. Economics, The expert of economics. Economics at National Metallurgical Academy of UkraineComputer Engineer Computer Science, Computer Engineer Computer Science at Welding college, Dnepropetrovsk, Ukraine
English, Ukrainian, Russian
Stackoverflow
9reputation
151reached
0answers
1question
Github Skills (9)
boto10
cloudwatch10
aws10
python10
mocking10
sagemaker10
testing9
logging6
buildout6
Programming languages (12)
Github contributions (5)
getmoto/moto
Oct 2021 - Nov 2022
A library that allows you to easily mock out tests based on AWS infrastructure.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 reviews, 4 commits, 5 PRs in 1 year 1 month
Contributions summary:Bogdan primarily contributed to the backend functionality of the `moto` library, focusing on the implementation of SageMaker service mocking. They added support for new SageMaker features such as Experiments, Trials, and Feature Groups, and also implemented pagination for listing experiments, trials, and components. Additionally, the user improved existing SageMaker components by adding functionality for associating and disassociating trial components, and adding unit filters to cloudwatch get_metric_data calls. These additions enhance the library's capability to mock AWS SageMaker and CloudWatch services for testing purposes.
ec2bototestinginfrastructureaws-infrastructure
bogdangi/moto
Sep 2021 - Jan 2024
A library that allows you to easily mock out tests based on AWS infrastructure.
Contributions:37 pushes, 13 branches in 2 years 4 months
testinginfrastructureaws-infrastructurestep-functionsaws
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