IT Software Engineer 4 – AI Architect (Testing)
Location: Chicago, IL (Hybrid – 3 days onsite)
Duration: 12-month contract
Pay Rate: $90 – $92hr
A leading enterprise organization is seeking an AI Architect for Testing to drive innovation across their quality engineering function. This role will define and implement AI strategy within software testing to improve quality, efficiency, and insight across the development lifecycle.
Key Responsibilities:
- Define and lead AI strategy for QA and testing initiatives
- Design AI-enabled solutions to enhance test coverage, automation, and defect detection
- Identify opportunities to apply AI across test case generation, test data creation, defect prediction, and root cause analysis
- Integrate AI capabilities into CI/CD and quality engineering workflows
- Evaluate and recommend AI/ML, GenAI, and test automation tools
- Establish governance standards for responsible AI usage within testing
- Architect integrations across LLMs, test repositories, defect tracking systems, and CI platforms
- Define and track metrics such as defect leakage, test efficiency, and release quality
- Lead pilot programs and scale AI testing capabilities across teams
- Mentor QA engineers and SDETs on AI-driven testing practices
Required Qualifications:
- Bachelor’s degree and 8+ years of experience in QA, test automation, or quality engineering
- 3+ years of experience designing or implementing AI/ML or GenAI solutions in enterprise environments
- Strong knowledge of SDLC, STLC, CI/CD pipelines, and testing methodologies
- Experience with AI technologies such as machine learning, NLP, LLMs, prompt engineering, or RAG concepts
- Proficiency with tools/languages such as Python, Java, JavaScript, SQL
- Hands-on experience with automation tools (Selenium, Playwright, Cypress, Appium, JUnit, TestNG, PyTest, etc.)
- Experience with platforms like Jira, Azure DevOps, GitHub, Jenkins, or GitLab
- Strong architectural design skills for scalable, secure solutions
Preferred Qualifications:
- Experience building AI copilots or assistants for QA teams
- Familiarity with MLOps / LLMOps and vector databases
- Cloud experience (AWS, Azure, or GCP)
- Experience in Agile environments and microservices architecture
- Exposure to DevOps tools, API gateways, and performance testing
- Strong documentation and communication skills
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