QA Automation Engineer

Job Overview ________________________________________

The QA Automation Engineer will be responsible for designing, developing, and executing automated test plans to ensure the reliability, performance, and accuracy of AI models, APIs, and deployment pipelines. This role focuses on end-to-end testing of AI systems, including model validation, API response accuracy, CI/CD verification, and data integrity checks. The ideal candidate should possess a solid foundation in Python-based test automation, with experience in testing AI workflows, backend APIs, and cloud-based integrations.

Key Responsibilities ________________________________________

• Develop and maintain automated test suites for AI models, REST APIs, and backend services.

• Perform functional, regression, and performance testing for AI-driven applications.

• Validate data preprocessing pipelines and ensure end-to-end data integrity for training and inference.

• Collaborate with AI Researchers and ML Engineers to verify model outputs, accuracy, and deployment stability.

• Execute CI/CD pipeline validation to ensure reliable deployment and rollback of models.

• Identify defects, maintain detailed test reports, and contribute to continuous quality improvement.

Required Skills & Qualifications ________________________________________

• 1–2 years of experience in QA Automation, preferably within AI/ML or data-driven environments.

• Strong programming skills in Python, with experience in test automation frameworks (PyTest, Unittest, Robot Framework).

• Understanding of RESTful API testing, Postman, and API performance validation tools.

• Familiarity with CI/CD tools (GitHub Actions, Jenkins, GitLab CI) for automated testing workflows.

• Working knowledge of data validation, schema checks, and test-driven development practices.

• Basic exposure to AI/ML model workflows, including model validation and inference testing.

Preferred (Good to Have) ________________________________________

• Experience with containerized testing environments (Docker/Kubernetes).

• Familiarity with Airflow or Celery for validating scheduled AI tasks.

• Exposure to MCP-based microservices, Agentic frameworks, or LangGraph workflows for AI application testing.

• Knowledge of Linux environments, shell scripting, and distributed test execution.

Soft Skills ________________________________________

• Strong analytical mindset with attention to detail and accuracy.

• Excellent documentation and bug-reporting skills.

• Collaborative and proactive attitude, working closely with AI Research and Development teams.

• Commitment to maintaining high quality, reliable, and secure AI systems.