QA Engineer

We’re looking for a QA Engineer who combines strong testing skills with hands-on experience using AI and test automation. We need someone who can analyze requirements, identify risks, validate AI-generated tests, and improve QA processes.
 

What you’ll do

• Analyze requirements before development, identify gaps, contradictions, and potential risks.
• Use AI to analyze Jira tickets, generate checklists and test scenarios, and validate the quality of AI-generated outputs.
• Test web applications, mobile apps (Android/iOS), and APIs.
• Verify user roles and permissions, data isolation between client companies, and other business-critical scenarios.
• Prepare test data and perform functional and regression testing.
• Create bug reports in Jira, verify fixes, and participate in release preparation.
• Develop and maintain automated tests, using AI to improve test automation efficiency.
• Improve prompts and AI pipelines, identify repetitive tasks for automation, and evaluate the results.
   

What we’re looking for

• 2–3 years of QA experience in a product team.
• Hands-on experience with test automation and using AI in QA processes.
• Knowledge of test design techniques and risk-based testing.
• Experience testing APIs using Postman or similar tools.
• Basic SQL knowledge: SELECT, JOIN, and database validation.
• Confident use of browser DevTools, including Network and Console.
• Experience testing mobile applications on Android and iOS, with a basic understanding of Android Studio and Xcode.
• JavaScript/TypeScript skills sufficient to read and write automated tests. Experience with Playwright is a plus.
• Experience with Jira, test documentation, and writing clear bug reports.
• Ability to critically evaluate AI-generated outputs and identify missing scenarios, duplicates, and incorrect checks.
• Independence, critical thinking, and the ability to work with incomplete requirements.
• English proficiency sufficient to read technical documentation.
   

Nice to have

• Playwright and experience running automated tests in CI.
• Experience with Charles Proxy, Proxyman, or similar network debugging tools.
• Experience with AI agents, MCP, and integrations with Jira/Confluence.
   

What success looks like

• Requirements and risks are analyzed before development begins.
• Checklists are prepared before testing starts.
• Testing is completed within agreed timelines, with a target SLA of one business day per ticket.
• Bug reports include reproducible steps, expected and actual results.
• Regression testing is completed before releases.
• AI and automation measurably improve testing quality and efficiency rather than creating additional overhead.