Andrii Liubimov
Florida, United States
Summary
🤩
Rockstar🎓
Top SchoolAndrii Liubimov is a seasoned software engineer with over 15 years delivering high-performance backend systems across capital markets, telecom, and collaboration platforms. He has led Java-based, low-latency trading systems and implemented DDD/CQRS architectures while building tooling for manual JVM memory management and zero-GC environments. Experienced in real-time streaming, messaging frameworks, and white-box testing, he mentors teams to produce maintainable, extensible systems that meet strict performance SLAs. Actively contributing to ML infrastructure on GitHub, he improved PyTorch/TensorFlow integration and query performance for the widely used DeepLake AI data platform. Now based in Florida, he is pivoting toward low-level systems engineering, automation of control, and robotics—bringing a rare combination of telecom-grade reliability and practical ML data engineering experience. Colleagues describe him as a pragmatic problem-solver who pairs deep systems thinking with a "child of the internet" curiosity.
11 years of coding experience
13 years of employment as a software developer
Igor Sikorsky Kyiv Polytechnic Institute
English, Russian, Ukrainian
Stackoverflow
497reputation
11kreached
7answers
5questions
Github Skills (18)
pytorch10
python10
machine-learning10
numpy10
datasets10
tensorflow10
data-science9
data-engineering8
vector-database7
mlops7
ruby-on-rails6
android-studio6
android6
akka6
cpu-usage6
Programming languages (5)
Github contributions (5)
activeloopai/deeplake
Oct 2021 - Jan 2022
Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
Role in this project:
ML Engineer
ML EngineerContributions:108 reviews, 14 commits, 40 PRs in 3 months
Contributions summary:Andrii primarily contributed to the integration and optimization of PyTorch and TensorFlow within the DeepLake ecosystem, addressing issues related to dependencies and data processing. They refactored the PyTorch integration, including improvements to data loading and shuffling, and fixed a bug related to non-writeable NumPy arrays. Further work involved improving the query performance and implementing a filtering system using a simple query language.
pythonreal-time-datadata-streamjupyter-notebookvisualize
aliubimov/waka-waka
Jan 2020 - Mar 2020
Contributions:98 commits, 96 pushes, 4 branches in 1 month
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