AI Quality Assurance (QA) Engineer
ROLE OVERVIEW
The AI QA Engineer will define and execute quality assurance strategies for AI and data-driven services used in government environments. The role covers functional, performance, data, model, bias, security, API and production-monitoring validation, with a strong focus on traceability and Responsible AI.
Educational Qualifications
- B.Tech. or M.Sc. in Computer Science, Data Science, or a related discipline.
- Certification in quality assurance, software testing, or test automation is preferred.
Experience
- 4–6 years of quality assurance experience for AI/ML systems, analytics platforms, or data-driven applications.
- Hands-on experience in functional, performance, data-validation, and model-output testing.
- Familiarity with testing approaches for NLP, computer vision, and data-centric applications.
Key Responsibilities
- Design and execute end-to-end testing strategies for AI services, covering functionality, performance, data quality, model accuracy, fairness, security, and compliance.
- Create test plans, test cases, test data, regression suites, and acceptance criteria tailored to AI/ML applications and government use cases.
- Validate model outputs against business requirements, reference datasets, accuracy thresholds, and expected operating conditions.
- Conduct fairness testing, bias detection, subgroup analysis, and Responsible AI compliance checks.
- Maintain defect logs, evidence, issue severity, root-cause details, and resolution tracking across development, staging, and production environments.
- Collaborate with data scientists, ML engineers, business analysts, and product teams to establish model-testing protocols and release gates.
- Build and execute automated test suites within CI/CD pipelines for AI services and data workflows.
- Validate APIs, microservices, integrations, data pipelines, ETL processes, and database outputs.
- Monitor production AI services for performance degradation, model drift, accuracy drift, data-quality failures, and compliance exceptions.
- Support privacy, access-control, encryption, audit, and security validation activities.
Technical Competencies
- AI Testing: model validation, output verification, bias detection, fairness testing, Responsible AI testing, and drift monitoring.
- Test Automation: Selenium, pytest, TestNG, Cypress, and CI-based automated test execution.
- Programming and Data: Python for test scripting, SQL for data validation, and basic statistical testing concepts.
- Testing Tools: Jira, TestRail, Postman, Jenkins, or equivalent tools.
- Data Validation: ETL testing, database testing, data-quality checks, reconciliation, and pipeline validation.
- Performance Testing: load, stress, scalability, latency, and throughput testing for AI and data-processing services.
- Security Testing: privacy validation, role-based access testing, encryption checks, and audit support.
- Cloud and API Testing: AWS, Azure, or GCP environments; REST, GraphQL, microservices, and integration testing.
Work Location: In person
