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Interested only in Machine Learning projects
I am a Machine Learning Engineer specializing in Computer Vision and related fields. With a rich background in developing and deploying computer vision algorithms, I've explored various applications, from conventional techniques to pioneering deep learning methodologies. Technical skills: • Python, C++, C, Assembler RH850 • Familiar with Tensorflow, Keras, Uber/Ludwig, OpenCV, Caffe, PCL, Octave/Matlab, ROS, CUDA etc • Algorithms (supervised/unsupervised machine learning and others) • Coding environments: Qt Creator, Eclipse-based IDEs, PyCharm, Visual Studio etc • Code versioning: Git, Mercurial, Perforce Soft skills: • Agile development methodologies, Scrum, Atlassian tools, UML • Good analytical research skills • Abilities to work without supervision, to adapt quickly to new technical environments • Self-starter, fast learner, team player
Moscow Institute of Physics and Technology (State University) (MIPT)
Master's degree, Applied Physics & Mathematics
January 1, 2010 – January 1, 2012
Taras Shevchenko National University of Kyiv
Bachelor of Science (BS), Biology/Biological Sciences, General
January 1, 2006 – January 1, 2010
NCube Ltd - software development outsourcing and outstaffing services
Machine Learning Engineer
December 1, 2019 – Present
Kyiv
GlobalLogic
ADAS/ML Engineer
October 1, 2017 – November 1, 2019
Kyiv
Intellias
C++/Qt/QML Developer
May 1, 2016 – August 1, 2017
Kiev
Symphony Teleca
Software Engineer
November 1, 2014 – April 1, 2016
Lodz Metropolitan Area
Samsung Electronics
Software Engineer
October 1, 2013 – October 1, 2014
Kiev
Smart Technologies Group
Flex Developer
July 1, 2012 – October 1, 2013
Kiev
Kyiv Scientific-Research Institute of Traumatology and Orthopedics
analyst engineer, biochemistry lab
September 1, 2011 – April 1, 2012
Kiev
Hadoop Platform and Application Framework (University of California, San Diego)
Coursera
June 24, 2026 – Present
SQL
Stanford Online
June 24, 2026 – Present
Heterogeneous Parallel Programming
Coursera
June 24, 2026 – Present
edX Honor Code Certificate for Foundations of Computer Graphics
edX
June 24, 2026 – Present
Machine Learning (Stanford)
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a strong academic background in Applied Physics & Mathematics and Biology, coupled with diverse industry experience across several companies (NCube, GlobalLogic, Intellias, Symphony Teleca, Samsung, Smart Technologies Group). This indicates a willingness to learn and adapt to different environments and technologies. However, the target role of 'Quality Assurance Engineer' is a significant shift from their primary experience in Machine Learning, C++ development, and embedded systems. While there's a mention of coordinating with QA testers, the core experience is not directly in QA methodologies, test automation, or quality engineering, which might impact cultural fit for a dedicated QA role.
Soft Skills & Operational Fit
The candidate's diverse project experience, including roles in ML, embedded systems, and front-end development, suggests adaptability and a broad technical perspective. The mention of collaboration with testers indicates an understanding of the QA process. However, without psychometric test results, specific soft skills like logical reasoning, work attitude, stress handling, and team collaboration cannot be objectively assessed.