Machine Learning QA Engineer
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Job Description
We are seeking a detail-oriented and technically skilled Machine Learning QA Engineer to ensure the quality accuracy and reliability of our ML models and systems. You will work at the intersection of quality assurance and machine learning developing automated and manual test strategies validating data pipelines and verifying ML model performance against business objectives.
Key Responsibilities:
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Design and implement test strategies for machine learning pipelines APIs and models.
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Develop automated testing frameworks for validating ML model inputs outputs and performance.
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Collaborate with ML engineers data scientists and DevOps teams to ensure seamless model deployment.
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Evaluate data quality and integrity throughout the ML lifecycle.
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Test for accuracy bias fairness reproducibility and drift in models.
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Validate and monitor ML models in production environments.
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Write detailed test plans test cases and quality documentation.
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Participate in code reviews and contribute to QA best practices in ML workflows.
Requirements:
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Bachelors/Masters degree in Computer Science Data Science Engineering or related field.
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Proven experience in QA/testing of machine learning systems or data products.
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Strong understanding of software testing practices (unit integration system regression).
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Experience with Python and testing frameworks (e.g. PyTest unittest).
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Familiarity with ML frameworks (e.g. TensorFlow PyTorch Scikit-learn).
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Understanding of data validation tools (e.g. Great Expectations TensorFlow Data Validation).
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Experience testing RESTful APIs and backend systems.
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Familiarity with CI/CD pipelines and version control systems (e.g. Git Jenkins).
Preferred Qualifications:
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Experience with MLOps tools like MLflow Airflow or Kubeflow.
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Exposure to cloud platforms (AWS/GCP/Azure) for model deployment and monitoring.
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Knowledge of model interpretability explainability and ethical AI principles.
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Experience working in Agile/Scrum teams.
Employment Type
Full Time
