Alex Voron
Software Engineering Manager at Intel Corporation
Munich, Bavaria, Germany
Summary
🤩
Rockstar🎓
Top SchoolAlex Voron is a software engineering manager based in Munich with 7 years of leadership experience building high-performance AI and cloud systems. At Intel he leads two teams contributing to OpenVINO and OpenCV, driving ARM CPU plugin development that enabled the fastest YOLO inference on Raspberry Pi 5 and was adopted by external Intel partners. He has a track record of cross-organizational optimization—delivering a 36% inference speedup for Fujitsu—and of improving delivery and reliability through scalable CI migrations and engineering process changes. Previously he led large, cross-regional teams at Avaya to migrate monoliths to microservices, increase delivery rates, and develop multiple engineering leaders. He pairs PhD-level technical depth in computer science with hands-on MLOps and backend contributions to major open-source AI tooling, and uniquely balances performance engineering with team mentorship to maintain sustained low attrition.
7 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Nizhniy Novgorod State Technical University (NSTU)
English, German
Github Skills (12)
arm10
inference10
architectures10
performance-optimization10
openvino10
architecture10
cprogramming-language9
c-language9
deep-learning8
build-automation7
computer-vision6
testing6
Programming languages (4)
Github contributions (5)
openvinotoolkit/openvino
May 2021 - Jan 2023
OpenVINO™ is an open source toolkit for optimizing and deploying AI inference
Role in this project:
Back-end & MLOps Engineer
Back-end & MLOps EngineerContributions:290 reviews, 12 commits, 182 PRs in 1 year 8 months
Contributions summary:Alex primarily contributed to the OpenVINO toolkit by modifying and updating core components related to AI inference optimization and deployment. They made changes to files related to file utilities, dependencies, and template plugins, and added functionality for ARM architecture, indicating focus on performance optimization, integration of new hardware and test configurations. They also addressed issues related to build and test failures, while also improving the library's ability to support multiple layers in various scenarios.
inference-enginepytorchmodel-optimizerdeep-learninggpu
alvoron/openvino
May 2021 - Apr 2025
OpenVINO™ Toolkit repository
Contributions:6 PRs, 836 pushes, 255 branches in 3 years 10 months
pytorchdeep-learninggpuopenvino-toolkitcomputer-vision
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