About the Role
The AI Quality Assurance Engineer is responsible for ensuring the quality, reliability, accuracy, and performance of AI/ML solutions throughout the development lifecycle. This role involves developing and implementing testing strategies for AI models, data pipelines, AI applications, and production deployments while ensuring that solutions meet business, technical, security, and regulatory requirements.
Ready to apply for roles like this?
Unlock the company name and direct application link. Subscribers get instant access to fresh jobs across Dubai, Abu Dhabi and Riyadh, many with visa support.
Unlock employer & apply directly
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 where applicable.
- 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.
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 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.
