QA Automation Engineer
Confirmed on the employer's own hiring board on Oct 9, 2026. First seen by Alion on Sep 17, 2026.
Join our growing engineering team as a QA Automation Engineer. In this role, you will drive the quality assurance process for our software products, focusing on automating tests and ensuring the reliability, performance, and fairness of AI-driven features. You will collaborate closely with developers, data scientists, and product teams to deliver high-quality, scalable solutions in an Agile environment.
Missions
- Design, develop, and maintain robust automated test frameworks and scripts for web, API, mobile, and AI/ML components.
- Create comprehensive test plans, strategies, and cases covering functional, regression, integration, performance, and end-to-end testing.
- Integrate automated tests into CI/CD pipelines (e.g., Jenkins, GitLab CI, GitHub Actions) to enable continuous testing.
Profil recherché
**Qualification*** Bachelor's degree in Computer Science, Software Engineering, or a related field (or equivalent experience).
* 3+ years of professional experience in software quality assurance, focused on test automation.
* Proven experience testing AI/ML systems (e.g., model validation, data pipeline testing, generative AI outputs).
* Strong programming skills in languages such as Python, Java, JavaScript, or similar for scripting automated tests.
* Hands-on expertise with automation tools and frameworks (e.g., Selenium, Cypress, Playwright, Appium, Robot Framework, pytest).
* Familiarity with AI/ML concepts (e.g., machine learning workflows, bias/fairness testing, TensorFlow/PyTorch ecosystems) and related testing tools.
* Experience with CI/CD tools, version control (Git), and issue tracking systems (Jira, Azure DevOps).
* ISTQB certification (Foundation or Advanced Level) or equivalent is a strong plus.
* Excellent analytical, problem-solving, and communication skills; ability to work in a fast-paced Agile/Scrum environment.
**Preferred Skills**
* Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
* Knowledge of performance testing tools (JMeter, Locust) and API testing (Postman, REST Assured).
* Exposure to AI-powered testing tools (e.g., Testim, Mabl, Applitools) or generative AI for test case creation.
* Background in data quality assurance for ML pipelines (e.g., Great Expectations, dbt testing).