We are looking for a QA Automation Engineer who not only writes automated tests but actively leverages AI to improve engineering processes, accelerate delivery, and increase automation coverage.
Specialist will join one of our US-based clients on an outstaff basis.
The team is international, so confident English is important for daily communication and collaboration.
What you'll work on
Design and build a UI test automation framework from scratch using Java, Selenium, TestNG, and Maven.
Define automation standards, framework architecture, and best practices.
Develop scalable and maintainable automated test suites.
Create automation strategies based on product requirements and business priorities.
Work with OAuth2 integrations and authentication flows.
Manage test documentation, reporting, and test execution processes.
Introduce and drive AI-powered approaches within QA and automation activities.
Build AI-assisted workflows for test generation, code development, debugging, maintenance, and documentation.
Evaluate and implement AI tools that improve engineering productivity and automation coverage.
What we're looking for
Strong Java Core (13-21): Collections, Streams, functional interfaces
Advanced OOP - Interfaces, Abstract classes, design patterns. The framework uses complex architectural patterns and you need to navigate them.
Selenium WebDriver - extensive UI automation experience, comfortable with dynamic elements.
TestNG + Maven - you've built and managed projects, not just run them.
Understanding of OAuth2 in the context of automated testing.
Experience with Jira and XRAY or a similar test management tool.
AI Experience (Critical Requirement)
We are looking for someone who has gone beyond simply using ChatGPT or Copilot for occasional coding tasks.
You should be able to demonstrate experience in:
Building AI-assisted engineering or testing workflows from scratch.
Using AI tools to generate, review, refactor, and maintain automation code.
Creating custom prompts, reusable workflows, agents, or internal AI solutions.
Identifying manual processes and replacing them with AI-driven automation.
Measuring and improving team productivity through AI adoption.
Understanding the risks, limitations, and validation requirements of AI-generated outputs.
Candidates whose AI experience is limited to basic prompt-and-response usage are unlikely to be a good fit.
