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Urgent! Zinnov - QA Automation Engineer - AI/ML Applications Job Opening In Bengaluru – Now Hiring Zinnov

Zinnov QA Automation Engineer AI/ML Applications



Job description

<p><p><b>Description :</b></p><br/><p><b>Key Responsibilities :</b><br/><br/>- Design, build, and maintain automation frameworks to test AI/ML applications, models, and services.<br/><br/>- Automate validation of model predictions, accuracy, and performance using test datasets.<br/><br/>- Develop automated regression and functional tests for data pipelines, APIs, and ML workflows.<br/><br/>- Integrate automation tests with CI/CD pipelines for continuous validation.<br/><br/>- Collaborate with Data Science and Engineering teams to define test strategies, acceptance criteria, and edge cases.<br/><br/>- Implement scalable and reusable automation scripts to improve testing efficiency.<br/><br/>- Ensure data quality validation as part of the automation framework.<br/><br/>- Report defects, track issues, and provide clear documentation of automation coverage and results.</p><br/><p><b>What Youll Need to Be Successful (Required Skills) :</b><br/><br/>- 3- 5 years of QA experience with at least 1+ years in automation for AI/ML applications.<br/><br/>- Strong programming skills in Python, Java, or JavaScript for test automation.<br/><br/>- Experience automating tests for ML workflows, APIs, and data pipelines.<br/><br/>- Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn) for model validation.<br/><br/>- Hands-on experience with automation tools/frameworks (e.g., Selenium, PyTest, Robot Framework, Cypress).<br/><br/>- Solid understanding of CI/CD pipelines (Jenkins, GitHub Actions, Azure DevOps, or similar).<br/><br/>- Knowledge of testing</p><br/><p><b>Education/ Certifications :</b><br/><br/>- Bachelors degree in computer science, Engineering, or a related field, or equivalent relevant experience.</p><br/><p><b>Preferred Skills :</b><br/><br/>- Exposure to AI/ML testing challenges such as bias detection, explainability, and performance benchmarking.<br/><br/>- Experience with cloud platforms (AWS, Azure, GCP) for deploying and testing ML models.<br/><br/>- Familiarity with data validation frameworks and synthetic data generation for ML testing.<br/><br/>- Experience with containerization and orchestration tools (Docker, Kubernetes) for test environments.</p><br/></p> (ref:hirist.tech)


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