AI QA Analyst / QA Prompt Engineer
Location: Lisle IL- Onsite
Employment Type: Full-Time / Contract
Role Purpose
The AI QA Analyst / QA Prompt Engineer will support enterprise project teams within the Quality Center of Excellence (QCoE) by leveraging AI-assisted test design, prompt engineering, and test asset management. This role focuses on accelerating high-quality test case creation, improving test coverage, and ensuring automation readiness for future Tosca-based execution.
Job Summary
We are seeking an entry-to-mid level QA professional to join the Quality Center of Excellence team in an AI-focused test design role. This role will support multiple project teams across the organization by using AI tools to accelerate and improve the creation of high-quality test cases, test scenarios, and coverage strategies.
The ideal candidate will have a solid foundation in software testing and quality assurance, an understanding of Agile delivery models, and a strong interest in applying prompt engineering techniques to generate effective, high-coverage test assets. This role will work closely with business analysts, project managers, testers, and quality leaders to transform requirements and business flows into optimized prompts and usable test cases with minimal iteration.
In addition to AI-led test design support, this person will help maintain quality assets in qTest and contribute to the long-term vision of integrating approved test cases into Tosca-based automation workflows.
Key Responsibilities
• Support project teams with AI-assisted test design based on user stories, requirements, functional specifications, technical specifications, future-state process documents, scenarios, and existing test assets.
• Analyze project documentation and prior sprint artifacts to create effective prompts that produce high-quality, high-coverage test cases with minimal back-and-forth.
• Act as an AI SME for assigned projects, owning AI-based test case generation and prompt refinement for those teams.
• Collaborate with business analysts, project managers, testers, developers, and other stakeholders to understand business processes, workflows, and testing needs.
• Design and refine prompts for tools such as ChatGPT, Copilot, and other LLM-based assistants to generate relevant and executable test cases.
• Review AI-generated test cases for completeness, logical coverage, traceability, testability, and business relevance.
• Optimize prompts and workflows to achieve 60-70% or higher first-pass execution validity with the fewest possible iterations.
• Support the creation of a scalable intake and delivery process where project teams submit documents such as FSDs, TSDs, FUTs, user stories, requirements, and existing test scenarios for AI-based test design support.
• Maintain selected and approved test assets in qTest for ongoing management and traceability.
• Partner with broader QA and automation teams to ensure AI-generated manual test cases are structured for future execution and possible conversion into Tosca automation.
• Apply QA best practices for test design, defect prevention, traceability, and quality governance.
• Participate in Agile ceremonies such as sprint planning, backlog refinement, stand-ups, and retrospectives, as needed.
• Continuously improve reusable prompt templates, team knowledge assets, and best practices for AI-enabled testing.
Required Qualifications and Core Skills
• Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent practical experience.
• 2-5 years of experience in software testing, QA, or quality engineering.
• Basic working knowledge of software testing principles, test design techniques, test execution, defect tracking, test management tools, and quality assurance processes.
• Experience working in Agile/Scrum environments with sprints, user stories, and backlog-driven delivery.
• Experience analyzing requirements, business processes, and user stories to derive test scenarios and test cases.
• Strong understanding of requirement analysis, test scenario creation, test case design, defect lifecycle, and regression and functional testing.
• Working knowledge of prompt engineering for QA and test design, including the ability to create structured prompts that improve output quality, reduce iteration, and increase test coverage.
• Ability to analyze user stories and historical test assets from prior sprints in order to design better prompts and generate more accurate, reusable test cases.
• Familiarity with AI-assisted documentation and content generation using tools such as ChatGPT, Copilot, or similar generative AI solutions in a testing or business context.
• Hands-on comfort with test management tools and asset maintenance activities; exposure to qTest is strongly preferred.
• Exposure to or willingness to learn Tosca automation, Tricentis products, and automation-readiness concepts for manual test cases.
• Tricentis, Tosca, qTest, or other testing tool certifications are preferred where available.
• Strong stakeholder communication skills, with the ability to work effectively with business analysts, project managers, testers, developers, and quality leaders across multiple projects.
• Strong written communication, documentation, analytical thinking, and attention to detail.
• Ability to manage work across multiple teams simultaneously and serve as an AI SME for assigned projects.
• Continuous improvement mindset and the ability to quickly learn project-specific processes, tools, business flows, and testing needs.
Nice to Have
- Experience working in a Quality Center of Excellence (QCoE) model
- Exposure to AI-driven quality transformation initiatives
- Interest in building scalable AI-enabled testing processes
