AI/ML Test Engineer
6 days ago
Responsibilities
- Design and execute test strategies for AI/ML models, ensuring data quality, accuracy, and reproducibility.
- Validate ML models against defined metrics such as precision, recall, accuracy, and F1 score.
- Develop automated test scripts and frameworks for model validation and monitoring.
- Perform functional, performance, and regression testing of AI-driven applications.
- Verify data pipelines and ensure data integrity from source to model input/output.
- Conduct bias and fairness testing to ensure ethical AI behavior.
- Work with data scientists to understand model logic, training datasets, and expected outcomes.
- Validate model deployment workflows (MLOps) and ensure smooth model versioning and retraining cycles.
- Integrate test automation into CI/CD pipelines for continuous model validation.
- Document test scenarios, defect reports, and model validation results.
- Research and implement new tools for AI model testing, explainability (XAI), and monitoring.
Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field.
- 3+ years of experience in software testing or QA, with at least 1+ year focused on AI/ML systems.
- Strong understanding of machine learning concepts, including supervised/unsupervised learning, NLP, and computer vision.
- Experience in testing ML pipelines, APIs, and data validation workflows.
- Proficiency in Python with libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch.
- Familiarity with MLOps tools like MLflow, Kubeflow, or AWS SageMaker.
- Knowledge of model interpretability and explainability frameworks (e.g., SHAP, LIME).
- Experience with automated testing tools and frameworks (e.g., PyTest, Robot Framework).
- Good understanding of REST APIs, JSON, and data serialization formats.
- Excellent problem-solving, analytical, and communication skills.
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