AI Quality Assurance Engineer

About the role

The AI Quality Assurance Engineer is responsible for ensuring the quality, reliability, accuracy, and performance of AI/ML solutions throughout their development lifecycle. This role designs and executes testing strategies for models, data pipelines, and AI applications to meet business, technical, security, and regulatory requirements.

Key responsibilities

• Develop and execute comprehensive QA and testing strategies for AI/ML solutions.
• Validate AI models for accuracy, consistency, robustness, and performance.
• Design test cases for machine learning models, AI applications, APIs, and data pipelines.
• Perform data quality, data validation, and data integrity testing.
• Test AI outputs for accuracy, bias, hallucination, reliability, and consistency.
• Conduct regression, integration, functional, performance, and automated testing.
• Establish quality gates and acceptance criteria for AI model deployment.
• Monitor model performance and identify model/data drift in production.
• Collaborate with Data Scientists, AI Engineers, Data Engineers, and business stakeholders.
• Automate testing processes and integrate QA activities into CI/CD pipelines.
• Document defects, test results, risks, and remediation activities.
• Support model governance, auditability, and compliance requirements.

Required profile

• Bachelor’s degree in Computer Science, Data Science, AI, Engineering, or a related field.
• 5+ years of experience in QA, AI/ML testing, software testing, or data quality.
• Strong understanding of machine learning and AI lifecycle processes.
• Experience with Python and automated testing frameworks.
• Knowledge of SQL and data validation techniques.
• Experience with APIs, cloud platforms, CI/CD, and version control.
• Understanding of ML model evaluation metrics and model monitoring.
• Strong analytical, problem‑solving, and communication skills.

Required skills

• Python
• Automated testing frameworks
• SQL
• APIs
• Cloud platforms
• CI/CD pipelines
• Version control systems
• ML model evaluation metrics
• Model monitoring and drift detection