Mikhail Sveshnikov
Software Engineer at Evidently AI
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolMikhail Sveshnikov is a software engineer with 11 years’ experience building production ML and data systems, currently contributing to Evidently AI from London. He brings deep back-end and MLOps expertise—having led the MLEM project at Iterative and worked as an ML architect and data engineering lead—focused on observability, data quality metrics, and reliable model deployment. An active open-source contributor, his work on evidentlyai/evidently improved metric support and testing for a widely used ML/LLM observability framework. He also teaches at the Higher School of Economics, blending practical engineering with academic grounding in big data and mathematics. Colleagues value him for translating complex ML requirements into testable, maintainable infrastructure and for quietly improving project robustness through thoughtful testing and refactors.
11 years of coding experience
8 years of employment as a software developer
Big Data, Big Data at Higher School of Economics
Английский
Github Skills (10)
monitoring10
pandas10
uptime-monitoring10
process-monitoring10
mlops10
data-monitoring10
performance-monitoring10
pytest10
python10
data-science8
Programming languages (7)
Github contributions (5)
evidentlyai/evidently
Dec 2020 - Apr 2025
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
Role in this project:
Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:50 reviews, 256 PRs, 846 pushes in 4 years 4 months
Contributions summary:Mikhail's commits focus on implementing and refining data quality metrics within the "evidentlyai/evidently" repository. Their work included adding support for metric results, creating new tests, and fixing existing tests. The changes involved modifications to existing code, suggesting an understanding of the project's architecture and the underlying machine-learning and LLM observability framework. Moreover, the user contributed to the testing framework and improved the code base.
ml-modelspythonmonitordiscordjupyter-notebook
mike0sv/pyjackson
Sep 2019 - Jun 2021
Contributions:1 release, 82 commits, 54 pushes in 1 year 8 months
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