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Job Description
Structured overview of role & requirementsAbout This Role
Design and implement automated testing strategies and validation frameworks for AI, data science models, and pipelines.
Develop and integrate statistical testing methods into QA workflows to ensure model accuracy, reliability, and monitor model drift.
Configure and manage testing infrastructure using platforms like KubeFlow and BentoML while optimizing testing processes for scalability in large-scale data environments.
Minimum Requirements
At least 7 years of quality assurance experience with hands-on work in data science and machine learning testing frameworks.
Advanced proficiency in Python, PySpark, and statistical testing methods including Hypothesis Testing, T-Test, Z-Test, and Regression.
Familiarity with machine learning frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet and data validation tools like Great Expectations and Evidently AI.
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a quantitative discipline.
Ideal Candidate Profile
Experienced in building and optimizing scalable automated QA pipelines for AI/ML production environments with CI/CD integration.
Strong expertise in statistical analysis and machine learning model evaluation with practical knowledge of probabilistic graph models and forecasting techniques.
Skilled in managing and troubleshooting testing infrastructure and processes within cloud-native architectures and distributed systems.