AI Quality Assurance Engineer

Role overview

Responsibilities

• Develop QA and testing strategies; validate models for accuracy, consistency, robustness, and performance.
• Design test cases for models, AI applications, APIs, and data pipelines; test data quality, validation, and integrity.
• Test AI outputs for accuracy, bias, hallucination, reliability, and consistency where applicable.
• Conduct regression, integration, functional, performance, and automated testing; establish deployment quality gates and acceptance criteria.
• Monitor production models for performance and model or data drift; collaborate with data, AI, engineering, and business teams.
• Automate tests and integrate QA into CI/CD; document defects, results, risks, and remediation; support governance, auditability, and compliance.

Requirements

• 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.
• 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.