Senior Software Qa Engineer

Autodesk Autodesk · Enterprise · Bangalore, India

Autodesk is seeking an AI-Powered Quality Automation Engineer to join their Upchain team. The role focuses on designing, developing, and evolving AI-assisted test automation strategies to improve system resilience, enable faster releases, and ensure high-quality software. Responsibilities include collaborating with agile teams, translating requirements into test strategies, exploring AI for test creation and analysis, developing automated tests, and promoting responsible AI use in quality engineering.

What you'd actually do

  1. Collaborate as part of an Agile/Scrum team to help ensure user stories are validated and acceptance criteria are met
  2. Translate customer workflows, product requirements, and user stories into practical test strategies and automated test coverage
  3. Design and execute end-to-end workflow tests across web applications, APIs, backend systems, and cloud-based services
  4. Explore and apply AI-assisted approaches to improve test creation, increase coverage, and reduce repetitive manual work
  5. Develop, maintain, and improve automated tests using modern frameworks and tools such as Playwright, TestComplete, Claude Code, or similar technologies

Skills

Required

  • software quality engineering
  • test automation
  • SDET
  • QA automation
  • validating web applications
  • backend systems
  • APIs
  • cloud-based software
  • automated tests
  • Playwright
  • Test Complete
  • Selenium
  • Cypress
  • JavaScript
  • TypeScript
  • Python
  • Java
  • API testing tools
  • REST APIs
  • Postman
  • Bruno
  • Agile development practices
  • QA methodologies
  • defect management processes
  • Jira
  • Confluence
  • Xray
  • problem-solving
  • communication
  • collaboration

Nice to have

  • AI-assisted software development or automation tools such as Claude Code, GitHub Copilot, or similar technologies
  • test data setup
  • response validation
  • applying AI or automation techniques to improve testing
  • defect analysis
  • engineering productivity
  • workflow-focused end-to-end testing for enterprise or customer-facing software systems
  • cloud-native or AWS-based systems
  • performance testing tools such as JMeter, BlazeMeter, or similar technologies
  • observability
  • monitoring
  • logs
  • reliability practices that support software quality
  • responsible AI practices
  • validation
  • governance
  • prompt quality
  • limitations of AI-generated outputs
  • PDM
  • PLM
  • manufacturing
  • engineering
  • enterprise workflow systems

What the JD emphasized

  • AI-assisted test automation strategies
  • AI can support test creation, execution, analysis, and maintenance
  • generative AI and automation techniques to support test generation, regression expansion, test data creation, failure analysis, and test maintenance
  • responsible use of AI in quality engineering, including validation and review of AI-generated outputs