Evgeny Talanin
AI Software Engineering Manager at Intel Corporation
Dubai, Dubai, United Arab Emirates
Summary
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Rockstar🎓
Top SchoolEvgeny Talanin is an AI software engineering manager based in Dubai with 19+ years in AI, ML and computer vision and eight years leading large-scale product engineering efforts. He currently heads a 50+ cross-functional team at Intel for OpenVINO, building scalable CI/CD, validation systems and release pipelines that support 1,200+ lab systems and multi-target hardware deployments. Known for turning test automation into a product, he created microservice dashboards and pre-commit systems that sped validation and developer feedback cycles by multiples. His background spans hands-on C++ computer vision, ADAS integrations and performance benchmarking, and he has contributed QA/test-infrastructure improvements to the widely used OpenVINO open-source toolkit. Evgeny combines technical depth with people-building—scaling teams from 10 to 50 with low attrition—and an uncommon blend of engineering, process design and business-minded leadership.
8 years of coding experience
11 years of employment as a software developer
Master, Business Administration and Management, General, Master, Business Administration and Management, General at State University of Nizhni Novgorod named after N.I. Lobachevsky (UNN)
Russian, English, German
Github Skills (14)
openvino10
test-automation10
testing10
c-language8
cprogramming-language8
optimisation7
optimization7
optimizer7
cmake7
computer-vision6
inference6
ai6
deep-learning5
deeplearning-ai5
Programming languages (3)
Github contributions (5)
openvinotoolkit/openvino
May 2020 - Jan 2022
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:45 reviews, 17 commits, 159 PRs in 1 year 8 months
Contributions summary:Evgeny's contributions primarily involve modifying and extending the testing infrastructure within the OpenVINO project. Their work includes skipping functional tests on specific hardware backends, and updating the tests configurations. They also addressed and reverted changes related to performance and debugging. Additionally, they modified the test infrastructure itself, as well as test configurations.
inference-enginepytorchmodel-optimizerdeep-learninggpu
evgenytalanin-intel/openvino
May 2020 - Oct 2024
OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository
Contributions:6 PRs, 90 pushes, 23 branches in 4 years 5 months
pytorchdeep-learningdeploymentopenvino-toolkitinference
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