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Niche AI/ML testing reduces applicant pool, but Bangalore metro and generic engineer title increase competition.
ML model testing skills are moderately industry-specific, requiring domain and tooling familiarity.
Requires ML testing expertise, Python and PyTest automation, but lacks explicit years requirement.
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Design and execute comprehensive test strategies, plans, and cases for AI/ML applications covering functional, integration, regression, system, API, and end-to-end testing.
Validate AI/ML model outputs against expected business requirements and performance metrics such as accuracy, precision, recall, F1-score, ROC-AUC, MAE, and RMSE.
Develop and maintain automation test scripts using Python and frameworks like PyTest; perform testing of REST APIs and AI/ML services using tools such as Postman.
Experience executing test strategies for AI/ML solutions with focus on model output validation and data quality.
Proficient in Python scripting and test automation frameworks such as PyTest.
Familiarity with Machine Learning concepts including supervised and unsupervised learning and relevant performance metrics.
Work Experience Required: Not explicitly mentioned in the JD.
Has a hands-on approach to integrating testing in AI/ML lifecycle with strong technical skills in Python and API testing tools.
Understanding of advanced ML model validation challenges such as bias, data drift, and model degradation.
Able to manage detailed testing processes for complex AI/ML systems ensuring robustness and accuracy of results.