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Mid-level, metro location and broad requirements increase competition, but niche ML-testing reduces applicant density.
Highly specialized ML-testing skills and model validation focus make background transferability limited.
Explicit 6–8 years plus mandatory Python, ML testing, and CI/CD skills enforce strict shortlisting.
Design and execute comprehensive validation and verification strategies for classical machine learning models across their lifecycle.
Develop and maintain scalable Python-based automated test frameworks integrating with CI/CD and ML-Ops pipelines for data, model, API, and batch workflows.
Define and validate monitoring and quality metrics for ML models in production, handle incident analysis, and collaborate across teams for release readiness and continuous improvement.
6-8 years of experience in software testing and test automation, including practical exposure to AI/ML or data-intensive applications.
Strong Python programming and hands-on experience with PyTest or equivalent test frameworks.
Bachelor’s degree in Engineering, Computer Science, Data Science, or a related discipline (B.E./B.Tech).
Experience with machine learning concepts, model lifecycle, evaluation metrics, data pipeline and API testing, CI/CD pipelines, ML-Ops knowledge, and cloud platforms like Azure, AWS, or Databricks.
Experienced in end-to-end ML quality assurance including risk-based test planning and validation across diverse ML use cases like classification, regression, clustering, and anomaly detection.
Proficient in building automation frameworks that support complex ML pipelines and integrate with operational monitoring and deployment workflows.
Capable of collaborating across multi-disciplinary teams (Data Science, ML Engineering, Product) in dynamic environments with onshore-offshore delivery models, ensuring actionable quality insights for business impact.