Quality Assurance Engineer – AI/ML

We are seeking a detail-oriented and proactive Quality Assurance Engineer to ensure the reliability, accuracy, and fairness of AI/ML models and data-driven applications. Who can design and execute tests, validate model performance, and collaborate closely with data scientists and ML engineers to deliver high-quality, production-ready solutions.

Key Responsibilities

• Design and execute test plans, test cases, and test scripts for AI/ML models and related applications
• Validate model performance using appropriate evaluation metrics (e.g., accuracy, precision, recall, F1-score)
• Perform data quality checks, including data consistency, completeness, and integrity validation
• Identify model biases, anomalies, and performance issues across different datasets
• Collaborate with Data Scientists, ML Engineers, and Developers to understand model logic and expected outcomes
• Conduct regression testing when models are retrained or updated
• Automate testing processes for model validation and data pipelines where applicable
• Monitor model outputs and ensure compliance with business and regulatory requirements
• Document defects, track issues, and provide clear reports on model performance and quality

Required Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field
• 2 years of experience in Software Quality Assurance or Testing
• Basic understanding of Machine Learning concepts and workflows
• Experience with testing tools and frameworks (e.g., Selenium, PyTest, JUnit, or similar)
• Familiarity with Python or SQL for data validation and testing
• Strong analytical and problem-solving skills

Preferred Skills

•  Experience testing AI/ML models or data pipelines
• Knowledge of model evaluation techniques and validation strategies
• Understanding of data preprocessing and feature engineering concepts
• Experience in API testing (e.g., Postman)
• Basic knowledge of cloud platforms (AWS, Azure, or GCP)

Added Advantage

• Experience with model monitoring and drift detection
• Understanding of AI ethics, bias, and fairness testing
• Exposure to CI/CD pipelines for ML workflows