Mykhailo Shastun
Senior Software Engineer at MODUS X
Lviv, Lviv Oblast, Ukraine
Summary
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Rockstar🎓
Top SchoolMykhailo Shastun is a Senior Software Engineer based in Lviv with 7+ years in software development and four years in hands-on industry roles building cloud-native and microservice systems. He has delivered end-to-end projects across .NET, AWS, and Azure ecosystems, designing architectures, CI/CD pipelines, and performant services for distributed order management and automation platforms. At Modus X and prior at Lohika and EPAM he shaped microservices from scratch, implementing robust patterns with MassTransit, gRPC, Docker, and observability tooling. An active backend contributor to the OpenVINO open-source project, he has implemented and optimized GPU kernels (e.g., DFT, GridSample, TopK) to boost AI inference performance — a detail that highlights low-level performance skills beyond typical backend work. Comfortable negotiating with stakeholders and mentoring small teams, he pairs pragmatic delivery focus with a willingness to dive into kernel-level optimization when projects demand it. Trained in system analysis at Ivan Franko National University of Lviv, he blends formal analytical thinking with practical engineering execution.
4 years of coding experience
5 years of employment as a software developer
Bachelor's degree System analysis, Bachelor's degree System analysis at Ivan Franko National University of Lviv
Ukrainian, English
Stackoverflow
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Github Skills (18)
unit-testing10
c-language10
testing10
gpu-programming10
optimizer10
system-testing10
openvino10
kernel10
optimisation10
kernel-mode10
qtest10
cprogramming-language10
optimization10
ai9
computer-vision8
Programming languages (1)
Github contributions (5)
openvinotoolkit/openvino
Jun 2022 - Nov 2022
OpenVINO™ is an open source toolkit for optimizing and deploying AI inference
Role in this project:
Backend Developer
Backend DeveloperContributions:106 reviews, 9 commits, 15 PRs in 5 months
Contributions summary:Mykhailo's primary contributions focused on implementing and optimizing GPU-based kernels and operations within the OpenVINO toolkit. They worked on implementing the Roll, Bucketize, IRDFT, DFT, CTCLoss, GridSample, Unique, and TopK operations, as well as adding and validating axes node functionalities. Their work involved writing kernel code, adding tests, and ensuring compatibility with various data types and formats, specifically focusing on improving OpenVINO's GPU performance.
inference-enginepytorchmodel-optimizerdeep-learninggpu
mhnap-lohika/openvino
Apr 2022 - Jul 2023
OpenVINO™ Toolkit repository
Contributions:101 pushes, 13 branches in 1 year 2 months
pytorchdeep-learninggpuopenvino-toolkitcomputer-vision
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