Senior Software QA Engineer

Job Requisition ID #

Position Overview

Autodesk is looking for an AI-Powered Quality Automation Engineer to join our Upchain team. Our team develops, maintains, and supports components of Autodesk Upchain, with a focus on PDM/PLM solutions and services that help customers manage complex product development workflows. In this role, you will help design, develop, and evolve AI-assisted test automation strategies that improve system resilience, support faster and more reliable releases, and contribute to high-quality software experiences for our customers. You’ll collaborate closely with engineering, product, and quality teams to shift quality earlier into the development process and explore opportunities where AI can support test creation, execution, analysis, and maintenance.

Responsibilities

• Collaborate as part of an Agile/Scrum team to help ensure user stories are validated and acceptance criteria are met
• Translate customer workflows, product requirements, and user stories into practical test strategies and automated test coverage
• Design and execute end-to-end workflow tests across web applications, APIs, backend systems, and cloud-based services
• Explore and apply AI-assisted approaches to improve test creation, increase coverage, and reduce repetitive manual work
• Develop, maintain, and improve automated tests using modern frameworks and tools such as Playwright, TestComplete, Claude Code, or similar technologies
• Use generative AI and automation techniques to support test generation, regression expansion, test data creation, failure analysis, and test maintenance
• Identify unstable or high-maintenance automation and help improve reliability, scalability, and maintainability
• Perform functional, regression, integration, exploratory, performance, and risk-based testing
• Evaluate software quality, performance, resilience, and scalability across important customer workflows
• Partner with engineering teams to identify validation needs earlier in the design and development process
• Support CI/CD quality practices and contribute to release readiness efforts
• Analyze test results, defects, automation failures, and quality trends to help identify risks and improvement opportunities
• Collaborate with cross-functional partners to define and evolve meaningful quality metrics and testing practices
• Promote thoughtful and responsible use of AI in quality engineering, including validation and review of AI-generated outputs
• Contribute reusable testing patterns, workflows, and automation practices that improve consistency and team productivity

Minimum Qualifications

• Bachelor’s degree in computer science, Software Engineering, Data Science, AI/ML, a related field, or equivalent practical experience
• 5+ years' Experience in software quality engineering, test automation, SDET, QA automation, or a similar role
• Experience validating web applications, backend systems, APIs, or cloud-based software
• Experience creating and maintaining automated tests using frameworks such as Playwright, Test Complete, Selenium, Cypress, or similar tools
• Familiarity with AI-assisted software development or automation tools such as Claude Code, GitHub Copilot, or similar technologies
• Experience with one or more programming or scripting languages such as JavaScript, TypeScript, Python, or Java
• Familiarity with API testing tools and practices, including REST APIs, Postman, Bruno, test data setup, and response validation
• Understanding of Agile development practices, QA methodologies, and defect management processes
• Experience with collaboration and tracking tools such as Jira, Confluence, or Xray
• Strong problem-solving, communication, and collaboration skills
• Ability to navigate ambiguity and develop practical, scalable quality approaches

Preferred Qualifications

• Experience applying AI or automation techniques to improve testing, defect analysis, or engineering productivity
• Experience designing workflow-focused end-to-end testing for enterprise or customer-facing software systems
• Familiarity with cloud-native or AWS-based systems
• Experience with performance testing tools such as JMeter, BlazeMeter, or similar technologies
• Familiarity with observability, monitoring, logs, or reliability practices that support software quality
• Understanding of responsible AI practices, including validation, governance, prompt quality, and limitations of AI-generated outputs
• Familiarity with PDM, PLM, manufacturing, engineering, or enterprise workflow systems

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