Middle Strong/Senior QA Engineer
First seen by Alion on Oct 23, 2026.
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.