Software Development
amgen
Job Description
- Build and promote test engineering practices, standards, and automation frameworks while fostering a strong quality-first and test automation culture.
- Design, develop, and maintain automated and manual test cases based on functional and non-functional requirements for enterprise, AI-driven, and workflow automation solutions.
- Perform testing activities in compliance with GxP and Computer System Validation (CSV) regulations, ensuring proper documentation, execution, traceability, and audit readiness.
- Create and maintain Requirement Traceability Matrices (RTMs) to ensure complete test coverage and regulatory compliance.
- Log, track, and manage defects using tools such as HP ALM, Azure DevOps, or Jira, ensuring clear documentation and end-to-end traceability.
- Execute and support User Acceptance Testing (UAT), Systems Integration Testing (SIT), Operational Qualification (OQ), regression testing, and validation activities across regulated environments.
- Collaborate with business stakeholders, developers, and validation teams to ensure defects are resolved effectively and quality standards are consistently achieved.
- Develop and maintain automation test scripts using tools such as UiPath Test Suite, Selenium, Postman, or similar technologies to improve test efficiency and coverage.
- Maintain comprehensive testing documentation, evidence packages, and validation artifacts to support compliance, inspections, and audit readiness.
- Perform end-to-end testing of LLM-powered, Agentic AI, and Custom GPT solutions, including prompt validation, response accuracy, grounding verification, hallucination detection, and Responsible AI compliance assessments.
- Validate AI-generated artifacts, automated workflows, and intelligent automation outputs to ensure reliability, consistency, data integrity, explainability, and compliance within GxP-regulated processes.
- Implement risk-based testing strategies for AI and automation platforms, focusing on quality, security, compliance, traceability, model performance, and regulatory requirements.
- Understanding end-to-end AI/ML lifecycle.