• Develop and execute test plans and test cases to validate machine learning models, APIs, and data pipelines.
•Ensure the accuracy, reliability, and performance of ML models in development and production environments.
•Automate testing processes for ML models, including data validation, model validation, and performance benchmarking.
•Perform exploratory and functional testing of ML applications, ensuring expected outputs and model behavior.
•Identify edge cases, biases, and inconsistencies in ML model predictions and training data.
•Collaborate with data scientists, ML engineers, and software developers to ensure quality at all stages of the ML lifecycle.
•Monitor ML models in production, validate retrained models, and track model drift over time.
•Conduct load and stress testing on ML inference pipelines to assess scalability and performance.
•Develop and maintain test automation frameworks for ML applications.
•Stay updated with best practices in ML testing and AI model validation.