Principal Software Quality Engineer

AspenTech•Bedford, MA

About The Position

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. The Role We are seeking a strategic and technical hands-on Principal Software Engineer to lead our Automation and Performance Engineering initiatives across the enterprise. As a Principal Software Quality Engineer you will join our growing AI team, this role is critical in driving quality, scalability, and velocity across Agile Release Trains (ARTs) within a SAFe environment, in support of advanced AI solutions You will collaborate with cross-functional teams to embed automation and performance best practices into the software development lifecycle, ensuring our systems meet the highest standards of reliability and customer satisfaction. This role offers an opportunity to play a key part in AspenTech’s AI-driven transformation and to act as a thought leader in software quality practices. The Principal Software Quality Engineer will establish and evangelize quality and test strategies across the full product lifecycle—from concept and design through implementation, testing, documentation, support, and maintenance—ensuring conformance to established requirements and standards. The role involves close collaboration with R&D and Product Management to proactively incorporate quality best practices, translate functional requirements into robust validation approaches, and drive continuous improvement in product quality for AI-powered solutions.

Requirements

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, large‑scale software systems.
Proven experience working at a senior technical level within Agile, Scrum, and SAFe development environments.
Demonstrated experience validating data‑driven and AI‑enabled systems, including handling non‑deterministic behavior, model lifecycle changes, and data dependencies.
Hands‑on experience with test automation frameworks, databases, and advanced testing tools across distributed systems.
Experience with cloud platforms (Azure, AWS), containers (Docker), and orchestration (Kubernetes).
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.

Nice To Haves

Exposure to AI/ML-based test generation or predictive performance modeling.

Responsibilities

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 LLM‑based systems, including prompt workflows, RAG pipelines, multi‑turn 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.