Сервіс пошуку роботи №1 в Україні
Богдан
QA-інженер
- Вік:
- 18 років
- Місто проживання:
- Кременчук
- Готовий працювати:
- Дистанційно, Кременчук
Контактна інформація
Шукач вказав: ТелефонЕл. поштуМесенджер
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Bohdan Korzh
QA Engineer / Manual + Automation • Remote
Telegram: @pominy | Email: [відкрити контакти ](див. вище в блоці «контактна інформація») / [відкрити контакти ](див. вище в блоці «контактна інформація»)
PROFILE
QA Engineer with about 1 year of experience in manual and automated testing of web applications and APIs,
gained on a hotel booking platform. Comfortable testing across Staging and Production environments, running
regression, smoke and exploratory testing, and writing test plans/strategies for new features. Experienced
with frontend (cross-browser, Playwright) and backend/booking logic (Postman, basic SQL for data
verification), and built a self-developed Python framework that automated end-to-end testing of an MCP
(Model Context Protocol) integration. Comfortable using AI coding tools (Cursor, Cline, Gemini) to speed up
development and debugging, and working within Agile/Scrum teams (daily standups, sprint planning, retro)
alongside developers and a PM.
SKILLS
Manual Testing: Frontend testing, cross-browser (Chrome, Firefox, Safari); regression, smoke
and exploratory testing across Staging/Production
Automation: Playwright (JavaScript) for UI test automation
API / Backend / Postman — writing and refactoring API tests, booking/reservation logic;
SQL: basic SQL (SELECT) for DB data verification
Test Docs & Test cases/checklists in Qase (manual and via MCP); writing test
Planning: plans/strategies for features
Process: Agile/Scrum — daily standups, sprint planning, retrospectives
Version Control: GitLab — storing test scripts/automation code, merge requests and code
review
Bug Tracking: Jira — logging and tracking bug reports
Debugging Tools: Chrome DevTools — analyzing and debugging network requests
Scripting / Python — built a custom test-automation framework for MCP testing
Tooling:
AI Tools: Cursor, Cline, Gemini — for writing/debugging automation code and
generating test cases via MCP
EXPERIENCE
QA Engineer (Manual + Automation) — Hotel Booking Platform
~ 1 year | Worked within a larger team alongside developers and a PM
• Wrote test plans/strategies for new features and worked in an Agile/Scrum team (daily standups, sprint
planning, retrospectives) alongside developers and a PM.
• Manually tested the website's frontend across multiple browsers (Chrome, Firefox, Safari) on Staging and
Production, running regression, smoke and exploratory testing; used Chrome DevTools to inspect and
debug network requests.
• Wrote and maintained frontend UI automated tests in JavaScript using Playwright; stored and versioned
test scripts in GitLab, with changes going through merge requests and code review.
• Tested backend functionality via Postman, focusing on the hotel reservation/booking flow (dates,
availability, booking creation); wrote and refactored API test scripts, and used basic SQL queries to verify
data directly in the database.
• Created and maintained test cases and checklists for new features (frontend and backend) in Qase, both
manually and through an MCP-based integration.
• Logged, tracked, and followed up on bug reports in Jira; used AI coding tools (Cursor, Cline, Gemini) to
speed up writing automation code and debugging.
• Tested an MCP (Model Context Protocol) integration and independently designed and built a Python
automation framework for it (see below).
KEY PROJECT — MCP TESTING AUTOMATION FRAMEWORK
Independently designed and built a Python-based framework that fully automated MCP (Model Context
Protocol) testing, cutting time per test case from ~2–3 min (manual, via Cline) to ~30 sec — roughly an 80%
reduction, running unattended in the background:
• Test cases were sourced from a simple XLSX file, so new cases could be added by following an existing
example — no coding required to extend coverage.
• The framework could launch a configurable number of parallel MCP sessions/runners, each automatically
running its own test case, with output sent to a separate AI "judge" model that returned a pass/fail verdict;
runner count, delay between test cases, judge model, and other parameters were configurable via a .env
file.
• Supported flag-based run configurations, e.g. running a specific test case by name, only cases that failed
in the previous run, or only cases tagged as critical.
LANGUAGES
Ukrainian — Native | English — B1 (Intermediate)
QA Engineer / Manual + Automation • Remote
Telegram: @pominy | Email: [
PROFILE
QA Engineer with about 1 year of experience in manual and automated testing of web applications and APIs,
gained on a hotel booking platform. Comfortable testing across Staging and Production environments, running
regression, smoke and exploratory testing, and writing test plans/strategies for new features. Experienced
with frontend (cross-browser, Playwright) and backend/booking logic (Postman, basic SQL for data
verification), and built a self-developed Python framework that automated end-to-end testing of an MCP
(Model Context Protocol) integration. Comfortable using AI coding tools (Cursor, Cline, Gemini) to speed up
development and debugging, and working within Agile/Scrum teams (daily standups, sprint planning, retro)
alongside developers and a PM.
SKILLS
Manual Testing: Frontend testing, cross-browser (Chrome, Firefox, Safari); regression, smoke
and exploratory testing across Staging/Production
Automation: Playwright (JavaScript) for UI test automation
API / Backend / Postman — writing and refactoring API tests, booking/reservation logic;
SQL: basic SQL (SELECT) for DB data verification
Test Docs & Test cases/checklists in Qase (manual and via MCP); writing test
Planning: plans/strategies for features
Process: Agile/Scrum — daily standups, sprint planning, retrospectives
Version Control: GitLab — storing test scripts/automation code, merge requests and code
review
Bug Tracking: Jira — logging and tracking bug reports
Debugging Tools: Chrome DevTools — analyzing and debugging network requests
Scripting / Python — built a custom test-automation framework for MCP testing
Tooling:
AI Tools: Cursor, Cline, Gemini — for writing/debugging automation code and
generating test cases via MCP
EXPERIENCE
QA Engineer (Manual + Automation) — Hotel Booking Platform
~ 1 year | Worked within a larger team alongside developers and a PM
• Wrote test plans/strategies for new features and worked in an Agile/Scrum team (daily standups, sprint
planning, retrospectives) alongside developers and a PM.
• Manually tested the website's frontend across multiple browsers (Chrome, Firefox, Safari) on Staging and
Production, running regression, smoke and exploratory testing; used Chrome DevTools to inspect and
debug network requests.
• Wrote and maintained frontend UI automated tests in JavaScript using Playwright; stored and versioned
test scripts in GitLab, with changes going through merge requests and code review.
• Tested backend functionality via Postman, focusing on the hotel reservation/booking flow (dates,
availability, booking creation); wrote and refactored API test scripts, and used basic SQL queries to verify
data directly in the database.
• Created and maintained test cases and checklists for new features (frontend and backend) in Qase, both
manually and through an MCP-based integration.
• Logged, tracked, and followed up on bug reports in Jira; used AI coding tools (Cursor, Cline, Gemini) to
speed up writing automation code and debugging.
• Tested an MCP (Model Context Protocol) integration and independently designed and built a Python
automation framework for it (see below).
KEY PROJECT — MCP TESTING AUTOMATION FRAMEWORK
Independently designed and built a Python-based framework that fully automated MCP (Model Context
Protocol) testing, cutting time per test case from ~2–3 min (manual, via Cline) to ~30 sec — roughly an 80%
reduction, running unattended in the background:
• Test cases were sourced from a simple XLSX file, so new cases could be added by following an existing
example — no coding required to extend coverage.
• The framework could launch a configurable number of parallel MCP sessions/runners, each automatically
running its own test case, with output sent to a separate AI "judge" model that returned a pass/fail verdict;
runner count, delay between test cases, judge model, and other parameters were configurable via a .env
file.
• Supported flag-based run configurations, e.g. running a specific test case by name, only cases that failed
in the previous run, or only cases tagged as critical.
LANGUAGES
Ukrainian — Native | English — B1 (Intermediate)
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