Principal Software Quality Engineer

The driving force behind our success has always been the people of AspenTech. What drives us, is our aspiration, our desire and ambition to keep pushing the envelope, overcoming any hurdle, challenging the status quo to continually find a better way. You will experience these qualities of passion, pride and aspiration in many ways - from a rich set of career development programs to support of community service projects to social events that foster fun and relationship building across our global community.

• Architect and maintain scalable, modular automation frameworks using Python, Java, or TypeScript.
• Drive automation across web, API, and backend services using tools like Playwright, Cypress, Selenium, Appium.
• Lead adoption of AI-assisted testing, model-based testing, and shift-left strategies.
• Implement LLM-assisted test generation (e.g., using Copilot, TestGen) to accelerate automation coverage.
• Integrate automation into CI/CD pipelines using GitHub Actions, Azure DevOps, or Jenkins.
• Integrate test results with quality dashboards and release gates (e.g., SonarQube, Power BI).
• Define and evolve performance engineering strategy across distributed systems, microservices, and cloud-native applications.
• Design and implement performance test suites using tools like K6, Gatling, JMeter, Azure Load Testing, and integrate with CI/CD pipelines.
• Collaborate with architecture and DevOps teams to identify bottlenecks and scalability issues, analyze system behavior under load.
• Establish performance baselines, SLAs, and drive continuous performance improvements across ARTs.
• Emphasizes "shift-right observability" and continuous validation across production environments.
• Collaborate with Product Owners, RTEs, and System Architects to embed performance and automation into PI Planning and backlog refinement.
• Contribute to PI Planning by identifying automation and performance objectives, dependencies, and capacity needs.
• Mentor engineers across teams; foster a culture of Built-In Quality and engineering excellence.
• Lead root cause analysis and resolution of complex performance and reliability issues.
• Define and execute quality strategies for LLMbased systems, including prompt workflows, RAG pipelines, multiturn conversations, and validation of accuracy, grounding, and safety.
• Build and maintain automated testing and regression frameworks for LLM APIs, prompts, and model/configuration changes, integrating quality checks into CI/CD pipelines.
• Collaborate with AI Engineering, Data Science, and Product teams to embed responsible AI practices, performance validation, and quality metrics throughout the LLM lifecycle.

• BS/MS in Computer Science, Information Systems, Engineering or other disciplines with strong software engineering foundations.
• 8-12 years of experience driving validation and quality strategies for complex, largescale software systems.
• Proven experience working at a senior technical level within Agile, Scrum, and SAFe development environments.
• Demonstrated experience validating datadriven and AIenabled systems, including handling nondeterministic behavior, model lifecycle changes, and data dependencies.
• Handson experience with test automation frameworks, databases, and advanced testing tools across distributed systems.
• Experience with cloud platforms (Azure, AWS), containers (Docker), and orchestration (Kubernetes).
• Exposure to AI/ML-based test generation or predictive performance modeling.
• Knowledge of security testing and compliance automation.
• Experience working in SAFe environments (e.g., PI Planning, ARTs, Lean Portfolio Management).
• Ability to define, evolve, and influence advanced testing strategies aligned with enterprise engineering standards and best practices.
• Deep expertise in applying quality engineering principles to AI workflows, including model validation, data pipeline testing, configuration change impact analysis, and regression strategies.
• Strong technical judgment with the ability to identify, assess, and communicate quality risks and mitigation approaches.
• Ability to operate as a trusted quality partner to Product, R&D, and AI/Data Science teams, influencing design and architectural decisions through a quality lens.
• Excellent written, verbal, and presentation skills, with the ability to communicate complex technical concepts clearly to diverse stakeholders.
• Continuous learning orientation, with awareness of emerging AI testing methodologies, tools, and industry best practices.