AI/ML Test Engineer

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.