
Principal QA Engineer- AI Testing (Chicago, IL)
- Chicago, IL
- $100,000-130,000 per year
- Permanent
- Full-time
- Define and implement a scalable test strategy and automation architecture across unit, API, UI, and performance layers.
- Collaborate with developers to design testable systems and write unit tests using TDD practices.
- Use AI tools like GitHub Copilot Pro+, CodiumAI, or Diffblue to generate, refactor, and improve test coverage and assertions.
- Develop and maintain reusable test frameworks for both desktop and mobile applications.
- Execute performance and load testing using JMeter, k6, or AI-enhanced tools such as Tricentis NeoLoad, LoadNinja, or Predator.
- Analyze business and technical requirements to create and maintain test cases, test plans, and test reports.
- Build internal tools or libraries to reduce repetitive work and increase QA reusability and intelligence.
- Track and manage issues using bug tracking tools and report on test progress through weekly QA metrics.
- Lead QA efforts across multiple projects/releases concurrently, including integration with AS400 or ERP systems.
- Provide training and mentorship on testing best practices and AI/automation standards to other team members.
- Raise red flags early and provide strategic QA input into project risk, quality, and release readiness.
- High school diploma or GED equivalent required
- Bachelor's degree in computer science or related field
- 8+ years of QA or SDET (software development, engineer and test) experience with strong automation and coding skills
- Solid programming experience in JavaScript, Python, or Java (with knowledge of .NET a plus)
- Hands-on experience in TDD (test driven development), unit testing frameworks (e.g., Jest, JUnit, PyTest), and co-authoring tests with developers
- Experience with GitHub Copilot Pro+, CodiumAI, or similar AI test coding assistants
- Test automation experience using Cypress, Playwright, Selenium/WebDriver, Postman, and RestAssured
- Experience in performance testing using JMeter, k6, or cloud-based/AI performance testing platforms
- Deep understanding of B2B eCommerce systems, distribution logic, and ERP integration (e.g., AS400)
- Strong familiarity with CI/CD pipelines (GitHub Actions, Azure DevOps, GitLab CI)
- Knowledge of test data design, XML/JSON, and SQL for backend validation.
- Built internal testing tools or reusable automation libraries
- Experience in AI/ML model validation or prompt testing
- Cloud-native testing exposure (AWS, Azure, GCP)
- Performance observability and root cause diagnostics