AI Quality Assurance

Designation: AI QA Engineer
Educational Qualification:
B. Tech / M.Sc. in Computer Science, Data Science, or related fields.
Certification in Quality Assurance or Test Automation preferred.
Experience:
4–6 years in quality assurance for AI/ML systems or data-driven platforms.
Experience in functional, performance, and data validation testing of AI models.
Familiarity with testing frameworks for NLP, CV, and data-centric applications.
Key Responsibilities:
Design and execute comprehensive testing strategies for AI services including functional, performance, and bias testing
Create detailed test plans, test cases, and regression testing suites specifically tailored for AI/ML applications and government use cases
Perform model validation testing, data quality assessment, and AI output accuracy verification against business requirements
Conduct fairness testing, bias detection, and ethical AI compliance validation for government AI applications
Maintain comprehensive logs of defects, issues, and resolution tracking across development, staging, and production environments
Collaborate with data scientists and ML engineers to establish testing protocols for model performance and reliability
Execute automated testing frameworks for continuous integration and deployment of AI services
Validate AI service integration points, API functionality, and data pipeline integrity
Monitor production AI systems for performance degradation, accuracy drift, and compliance violations
Technical Competencies:
AI Testing: Model validation, bias detection, fairness testing, AI output verification, and responsible AI compliance testing
Test Automation: Selenium, pytest, TestNG, Cypress for automated testing frameworks and continuous integration
Programming Languages: Python for test scripting, SQL for data validation, basic understanding of R for statistical testing
Testing Tools: Jira, TestRail, Postman for API testing, Jenkins for CI/CD testing pipelines
Data Validation: Data quality testing, ETL testing, data pipeline validation, and database testing techniques
Performance Testing: Load testing, stress testing, and performance monitoring for AI services and data processing systems
Security Testing: Data privacy validation, access control testing, encryption verification, and compliance audit support
Cloud Testing: AWS, Azure, GCP testing environments, cloud service validation, and multi-environment testing strategies
API Testing: REST API testing, GraphQL testing, microservices testing, and integration testing methodologies

Work arrangement
No