Job Description: Automation & AI Quality Engineer
Role Overview
We are looking for an Automation QA Engineer who is ready to bridge the gap between traditional automation and AI-driven quality engineering. You will not only build robust test frameworks but also leverage AI tools to increase test coverage, reduce maintenance overhead, and accelerate our CI/CD pipeline.
Key Responsibilities
- AI-Enhanced Frameworks: Design and maintain automation frameworks (e.g., Selenium, Playwright) while integrating AI-powered plugins for self-healing locators to reduce flaky tests.
- Generative QA: Utilize Large Language Models (LLMs) and GenAI tools to automatically generate test cases, edge-case scenarios, and synthetic test data.
- Shift-Left Intelligence: Implement AI tools that analyze code changes to predict which tests are most likely to fail, optimizing our test suites for faster feedback.
- Visual Regression: Deploy AI-based visual testing tools (like Applitools or Percy) to detect UI inconsistencies that traditional pixel-matching tools miss.
- Performance Insight: Use machine learning models to analyze system logs and performance metrics to identify bottlenecks before they impact production.
Technical Skills & Qualifications
- Traditional Stack: Proficiency in Java, Python, or JavaScript and experience with frameworks like PyTest, TestNG, or Cypress.
- AI Tooling: Hands-on experience or strong interest in AI testing platforms (e.g., Testim.io, Mabl, or Functionize).
- Prompt Engineering: Ability to use GenAI (ChatGPT, GitHub Copilot) to assist in writing scripts and documenting test plans.
- Data Literacy: Basic understanding of how AI models are trained and validated, specifically for testing AI-integrated features within our own product.
