AI Quality Assurance Engineer

Job Title: AI Quality Assurance Engineer Experience: 5+ Years Job Type: Full-Time Job Summary Responsible for ensuring the quality, reliability, accuracy, and performance of AI/ML solutions throughout the development lifecycle. The role develops and implements testing strategies for AI models, data pipelines, AI applications, and production deployments while ensuring solutions 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 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.

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.