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 RoleWe 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.Your Impact
- 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.
- 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).
- 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.
Skills Required
- BS/MS in Computer Science, Information Systems, Engineering or related
- 8-12 years experience driving validation and quality strategies for large-scale software systems
- Proven experience in Agile, Scrum, and SAFe development environments
- Demonstrated experience validating data-driven and AI-enabled systems, including non-deterministic behavior and model lifecycle changes
- Hands-on experience with test automation frameworks and tools (Playwright, Cypress, Selenium, Appium)
- Experience writing automation using Python, Java, or TypeScript
- Experience integrating automation into CI/CD pipelines (GitHub Actions, Azure DevOps, Jenkins)
- Experience with performance testing tools (K6, Gatling, JMeter, Azure Load Testing) and performance engineering for distributed systems
- Experience with cloud platforms and container orchestration (Azure, AWS, Docker, Kubernetes)
- Experience building LLM/AI testing strategies, prompt/workflow validation, RAG pipelines, and LLM API testing
- Knowledge of security testing and compliance automation
- Strong communication, mentorship, and ability to influence product and architecture decisions
- Exposure to AI/ML-based test generation or predictive performance modeling
Aspen Technology Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Aspen Technology and has not been reviewed or approved by Aspen Technology.
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Healthcare Strength — Health coverage is described as strong, with comprehensive medical, dental, and vision plans and high-quality carrier options. Several recent remarks characterize the health insurance as "great" or "amazing," with low copays noted in some cases.
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Parental & Family Support — Maternity and paternity leave receive consistently positive mentions and are characterized as well-reviewed. Company materials also highlight family-oriented benefits alongside core coverage.
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Leave & Time Off Breadth — The package includes vacation/PTO, paid holidays, and sick leave, with multiple indications of generous paid time off. Dedicated volunteer hours and well-regarded leave policies reinforce breadth in time-away benefits.
Aspen Technology Insights
What We Do
AspenTech is a global leader in asset optimization software helping the world’s leading industrial companies run their operations more safely, efficiently and reliably – enabling innovation while reducing waste and impact on the environment. AspenTech software accelerates and maximizes value gained from digital transformation initiatives with a holistic approach to the asset lifecycle and supply chain. By introducing effective AI modeling to traditional principles of process engineering, AspenTech delivers a faster and more accurate analysis of efficiency and performance boundaries. The real-time data and actionable insights delivered by our software help customers push the boundaries of what’s possible.

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