Boomi is a fast-growing company that connects everyone to everything, anywhere. As a Senior Software Quality Engineer, you will bridge traditional software quality engineering with cutting-edge AI quality practices.
Requirements
- Design and execute comprehensive test plans for backend services, APIs, and microservices architectures.
- Partner with Backend Engineers, AI Engineers, and Product teams to understand requirements and identify quality risks early in the development cycle.
- Develop automated testing frameworks using tools like Pytest, Playwright, unittest, and integration testing libraries.
- Advocate for quality-first practices, influencing architectural decisions and embedding testing into the development lifecycle.
- Design and implement automated evaluation frameworks for Generative AI features, including LLM / SLM model testing, prompt/ output validation, and behavioral assessment of agentic workflows.
- Develop quality metrics and evaluation methodologies for LLM-based applications, assessing accuracy, consistency, reliability, quality of AI/ML models.
- Create and maintain curated evaluation datasets and synthetic test data that cover edge cases, adversarial scenarios, and real-world variability.
- Implement performance testing, load testing, and reliability testing for production backend services and AI inference pipelines.
- Define and champion quality standards, best practices, and testing methodologies for both traditional backend systems and AI applications.
- Conduct code reviews with a focus on testability, quality patterns, and maintainability.
- Mentor junior quality engineers, sharing expertise in automation frameworks, AI testing approaches, and quality engineering principles.
- Communicate quality insights, risk assessments, and test results effectively to technical and non-technical stakeholders using tools like Jira and Confluence.
Benefits
- Comprehensive test plans for backend services, APIs, and microservices architectures.
- Partnership with Backend Engineers, AI Engineers, and Product teams.
- Automated testing frameworks using tools like Pytest, Playwright, unittest, and integration testing libraries.
- Quality-first practices and architectural decisions.
- Automated evaluation frameworks for Generative AI features.
- Quality metrics and evaluation methodologies for LLM-based applications.
- Evaluation datasets and synthetic test data.
- Performance testing, load testing, and reliability testing for production backend services and AI inference pipelines.
- Quality standards, best practices, and testing methodologies for traditional backend systems and AI applications.
- Code reviews with a focus on testability, quality patterns, and maintainability.
- Mentorship and knowledge sharing with junior quality engineers.
- Effective communication of quality insights, risk assessments, and test results.
