About
We are looking for a QA Automation Engineer to join a team building modern AI-powered applications. In this role, you will be responsible for designing and maintaining a robust automated testing ecosystem across frontend, backend, APIs, and end-to-end workflows. You'll work closely with engineering teams to ensure product quality, prevent regressions, and establish measurable quality gates for both traditional software components and LLM-powered features.
Relocation package
Job rotation
Learning through Arnia Academy
Attractive projects
Flexibile working hours
Performance bonuses
Medical benefits
Trainings
Competitive compensation package
Referral program
International work experience

Requirements

  • 3+ years of experience in QA Automation for production systems
  • Strong hands-on experience with Python testing using Pytest
  • Solid experience with frontend test automation
  • Experience building and maintaining E2E test suites using Playwright (or Selenium)
  • Experience with API testing, including REST APIs, schema validation, authentication, and error scenarios
  • Experience working with SQL databases, preferably PostgreSQL, and managing test data
  • Experience with CI/CD pipelines using tools such as GitHub Actions, Azure Pipelines, or Jenkins
  • Practical understanding of testing non-deterministic outputs generated by LLM-based applications
  • Strong analytical and problem-solving skills
  • Ability to collaborate effectively within Agile development teams

Responsibilities

  • Design, implement, and maintain automated frontend testing
  • Build and extend API and integration test coverage for backend services and database workflows
  • Develop and maintain end-to-end test suites using Playwright for critical user journeys
  • Create stable mocking and fixture strategies using tools such as MSW, pytest fixtures, and predefined test datasets
  • Configure and maintain CI quality gates for linting, unit, integration, and E2E testing, code coverage, and reporting
  • Expand automated test coverage across untested UI components and workflows while preventing regressions
  • Establish and maintain LLM evaluation frameworks using golden datasets and measurable quality gates
  • Monitor model output reliability, detect quality drift, and support release decisions based on predefined quality thresholds
  • Continuously improve test maintainability by reducing flakiness and optimizing execution times
  • Collaborate with developers and product teams to drive quality throughout the software development lifecycle

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General application

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