Vadim Demkiv

Next-Gen Tech Industry Transition: After the mass layoffs at Black Forest Games, ultimately decided to switch industries, and prepared with self-directed learning and regular coding practice.

• Traversal System Ownership: Implemented the vast majority of the traversal system of a prestigious Teenage Mutant Ninja Turtles (TMNT) game, one of its most important pillars. (C++, UE5) • Documentation Excellence: Authored and maintained detailed, high-quality documentation and an internal development blog, both highly praised as "the best I’ve seen so far". • Innovative Coding Practices: Lead the integration of ClangFormat and GitHub Copilot to optimize the coding department's workflow efficiency.

Games Industry Transition: Prepared for roles in the games industry, enhancing skills through self-directed learning, leveraging over 25 years of gaming experience and a strong interest in Unreal Engine 5.

• Performance Excellence: Achieved maximum scores in all metrics in Google Lighthouse, GTmetrix, Rank Math, and SSL Labs with my one-pager website. • WordPress Customization: To achieve these exceptional results, developed custom plugins, themes, and blocks. (CSS, JavaScript, PHP, HTML)

Web Industry Transition: Completed over 60 LinkedIn Learning courses, several books, and practical exercises to develop new technical expertise.

• Computer Vision (CV) Prototyping: Built a CV AI training framework for a generative model, achieving subjective ground truth-level accuracy in select examples. (Python, C++, Keras, TensorFlow) • Innovative Patent Work: Played a pivotal role in the research and development of 2 patents for image processing with AI. (Python, Amazon SageMaker) • Hack Day Award Winner: Led a team of 3 and built an AI image processing feature to win the “Closest to Shippable” award.

Innovative Tools Development: Introduced an Excel-based application for the analysis of intranet usage for data-driven prioritization of maintenance. (VBA)

Focus: Computer vision and deep learning. Thesis: Semantic mapping of walkable areas with stereo camera images and visual odometry.