AI Architect; QA Automation

Role Summary

• WD is looking for an architecture that is able to explain what can be done with AI
• WD needs someone who can explain the backend of AI and how they would implement it.
• What would be the candidate's approach to incorporate AI

• We’re looking for an AI Architect to lead how our testing organization uses AI to improve quality, speed, coverage, and insight across the software delivery lifecycle.
• This person will define the AI strategy for QA and testing, design AI-enabled testing solutions, guide tool selection and integration, and help teams adopt AI responsibly in day-to-day testing activities.

• Define the AI vision and architecture for the testing function.
• Identify testing activities that can be improved with AI, including: test case generation, test data creation, automated test maintenance, defect prediction, root cause analysis, log and failure analysis, requirements-to-test traceability, risk-based test prioritization, intelligent regression selection.
• Design and implement AI-enabled frameworks for: functional testing, API testing, UI automation, performance testing, security testing support, exploratory testing augmentation.
• Partner with QA, developers, Dev Ops, product managers, and data teams to embed AI into CI/CD and quality engineering workflows.
• Evaluate and recommend AI/ML, GenAI, and test automation tools.
• Establish standards for AI governance, privacy, security, explainability, and responsible usage in testing.
• Create architecture for integrating LLMs, test repositories, defect systems, observability tools, and CI platforms.
• Define metrics to measure the effectiveness of AI in testing, such as: defect leakage, automation coverage, test execution efficiency, flaky test reduction, mean time to diagnose failures, release quality trends.
• Mentor QA engineers and SDETs on AI-assisted testing practices.
• Lead pilots, proofs of concept, and scaled rollout of AI testing capabilities.
• Ensure compliance with enterprise policies for data handling and model usage.

• Working with team; team consists of 20+ folks.

Chicago Office: 540 W Madison Street, Chicago, IL 60661

• Years of experience: 8-10 yrs experience
• Degree requirement:
  Bachelor’s degree and 8+ years of experience

(Required)

• 8+ years in software testing, QA automation, quality engineering, or test architecture.
• 3+ years designing or implementing AI/ML or GenAI-based solutions in enterprise environments.
• Strong understanding of: software testing methodologies, test automation frameworks, SDLC/STLC, quality engineering practices.
• Experience with AI technologies such as: machine learning models, NLP, LLMs, prompt engineering, retrieval-augmented generation concepts.
• AI agents/workflow automation.
• Experience integrating with platforms such as Jira, Azure Dev Ops, Git Hub, Jenkins, Git Lab, or cloud test platforms.
• Knowledge of test analytics, data pipelines, and observability/log analysis tools.
• Strong architecture and design skills for scalable, secure enterprise solution.
• Experience building AI copilots or assistants for QA teams.
• Familiarity with vector databases, model evaluation, and MLOps/LLMOps concepts.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Understanding of regulated environments and compliance requirements.
• Certifications in test architecture, cloud, AI/ML, or enterprise architecture.
• Hands on experience with Python.
• Development experience in AWS Cloud technology.
• Experience in TDD, continuous integration, code review practice is strongly desired.
• Experience with Apigee or other API gateways is a plus.
• Experience with Dev Ops concepts and tools (e.g., CI/CD, Jenkins, Git).
• At least 2 years working on an Agile team with a solid understanding of Agile/Lean practices.
• Understanding of a micro service Architecture.
• Experience load and performance testing.
• AI strategy for testing.
• Quality engineering leadership.
• GenAI and LLM application design.
• Stakeholder communication.
• Governance and risk management.
• Team enablement.

(Required)

• Ability to adapt quickly to a complex environment.
• Pro‑active, flexible and creative.
• Very strong communication skills and the ability to collaborate with developers and business users.