AI Quality Engineer Lead/AI Lead Quality Analyst- 100% Remote

We are seeking a Lead QA Automation Engineer with hands-on experience in AI-powered testing and automation frameworks. You will design, implement, and maintain robust automated test suites while leveraging AI/ML tools to improve coverage, defect prediction, and test efficiency across the SDLC.

Key Responsibilities

• Develop and maintain automated test scripts for web, mobile, and API applications using tools like Selenium, Cypress, Playwright, or TestNG.
• Integrate AI/ML frameworks to optimize testing processes (e.g., intelligent test case generation, flakiness detection, root cause analysis).
• Implement continuous testing pipelines within CI/CD environments (e.g., Jenkins, GitHub Actions, GitLab CI).
• Collaborate with developers, data scientists, and product managers to define test strategies for AI/ML-driven features and models.
• Validate data pipelines, model outputs, and predictions for accuracy and fairness.
• Utilize AI-based testing tools such as Testim.io, Applitools, Functionize, or Mabl to enhance automation reliability.
• Monitor and report on test metrics, coverage, and defect trends using AI analytics dashboards.
• Participate in exploratory and regression testing using a risk-based approach.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 3–7 years of QA experience with a focus on automation testing.
• Experience with AI-assisted testing tools or integrating ML algorithms for test optimization.
• Strong knowledge of Python, Java, or JavaScript for automation scripting.
• Familiarity with REST API testing (e.g., Postman, RestAssured).
• Proficiency in CI/CD pipelines and version control (Git).
• Understanding of data validation and model testing in AI workflows.

Preferred Skills

• Experience with AI/ML model validation, data drift detection, or synthetic data generation.
• Knowledge of cloud environments (AWS, Azure, Google Cloud Platform) and container orchestration (Docker, Kubernetes).
• Exposure to AI-driven DevOps (AIOps) or MLOps practices.
• Certification in ISTQB, AWS AI Practitioner, or similar is a plus.

Example AI Tools/Technologies

• AI Testing Tools: Testim.io, Applitools Eyes, Functionize, Mabl
• ML Frameworks: TensorFlow, PyTorch, Scikit-learn (for model validation)
• Automation Tools: Selenium, Playwright, Cypress, PyTest
• Monitoring & Analytics: Datadog, Grafana, ELK with AI anomaly detection