Maksym Krutskykh
Senior Software Quality Architect @Ghgsat
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WORK HISTORY
Senior Software Quality Architect @Ghgsat
Ottawa, ON, CA
Prompt Engineering: designed and refined prompts for large language models (GPT*, Claude) to generate test cases, reducing manual test‑case authoring time by 30 %. LLM‑Assisted Test Generation: Integrated Claude API with Pytest to auto‑create end‑to‑end UI tests from product specifications, increasing test coverage across 5+ platforms. Architecting and implementing a robust Data Security and Quality Automation Framework using Python, Pytest, and Great Expectations to ensure satellite geospatial data integrity and consistency. The framework leveraged extensive DataFrame operations with Pandas to efficiently preprocess, clean, and transform datasets before applying validation rules. Developing PlayWrite framework with testing UI satellite operational web app. Both frameworks was successfully integrated into the Continuous Integration (CI) pipeline using GitLab, enabling automated validation as part of the development lifecycle.
EDUCATION
State University of Uman', Ukraine
Master's degree
University of Economics, Varna, Bulgaria
ABA, International Relations
SKILLS
ABOUT MAKSYM KRUTSKYKH
AI (LLM) Models (Copilot, Claude, and GPT‑5) Operations :Effectively utilizing Large Language Models (LLMs) and advanced automation platforms to significantly enhance QA test automation, CI/CD pipelines, and overall software delivery. By harnessing LLM capabilities, prompt engineering generates intelligent test cases, LLM‑assisted test generation, and synthetic test data for privacy‑safe coverage, while also producing automated documentation that accelerates test creation and captures edge cases often missed manually.Applying model‑based testing and natural‑language test specification parsing to turn user stories and BDD scenarios into executable scripts, and leveraging AI‑enhanced defect classification to auto‑prioritize bugs. With AI‑driven performance forecasting, load spikes are predicted and proactive stress tests are scheduled.Automation workflows integrate testing tools, version‑control systems, and communication platforms. These workflows embed responsible AI practices (prompt safety, bias checks) and Data‑Ops for AI‑generated artifacts, ensuring reproducibility and compliance. Real‑time updates and notifications keep all stakeholders informed of testing progress and results—for example, automating regression test execution triggered by code commits, delivering immediate feedback to developers, and reducing the time between code changes and deployment: UI/API/Mobile Test Automation/Data Warehouse Governance and ETL Data Quality Management :Architecting and providing implementation of scalable QA Automation Frameworks utilizing multiple tools and languages. JS/TS with PlayWrite, Cypress, Protractor, Jest, Puppeteer; UI and API testing with Joi/Selenium/Java and Cucumber/Behat /PHP bundles; mobile apps test automation with Appium (Android and iOS); Data QA Automation Frameworks utilizing Python, Pytest, and Great Expectations, ensuring data integrity and consistency with Pandas used for preprocessing, cleaning, and transforming datasets for structured validated workflows; Vehicle electronic control unit (ECU) telematics VGM/OBDII hardware testing with CANalyser/Vector: Software Engineering & Development :Programming - CodeWars Rank, GitHub RepositoryPython - years of developing custom automation frameworks and FastAPI projects.Flutter, Dart - designed, developed and released own apps to App Stores (Web, iOS, Android).Data - Data Governance Certificates in Licenses & Certifications Section.JS - VueJS projects and custom automation frameworks: AI Quality Audit :Human‑centered QA remains essential.
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