Generative AI Test Architect

Role:Generative AI Test Architect
Location:Chicago, IL
Duration: 12+ Months

Position Overview
Client is seeking an experienced AI Architect for Testing to lead enterprise-wide transformation initiatives focused on applying Artificial Intelligence and Generative AI within Quality Engineering and Software Testing organizations.

This role will drive the strategy, architecture, and implementation of AI-enabled testing solutions designed to improve software quality, accelerate testing efficiency, enhance automation capabilities, and modernize QA operations across the SDLC/STLC lifecycle.

The ideal candidate will possess a strong background in QA automation, enterprise testing architecture, AI/ML technologies, and modern software engineering practices. This individual will partner closely with QA teams, developers, DevOps engineers, and product stakeholders to integrate AI-driven capabilities into enterprise testing ecosystems.

Work Location 100% Remote within the United States Candidates located near Chicago may be expected to follow a hybrid schedule: o Approximately 2 3 days onsite per week o Or as needed based on business requirements

Key Responsibilities
AI Strategy & Testing Transformation
Define and lead the AI vision and strategy for enterprise testing organizations
Establish AI adoption roadmaps and best practices for QA teams
Drive AI-enabled transformation initiatives across testing and quality engineering functions
Identify opportunities where AI can improve testing efficiency, coverage, and quality

AI-Driven Testing Solutions
Design and implement AI-powered solutions for:
Test case generation
Test data creation
Automated test maintenance
Defect prediction
Root cause analysis
Failure and log analysis
Intelligent regression testing
Risk-based test prioritization
AI-assisted automation workflows

Framework & Architecture Design
Design scalable AI-enabled testing frameworks supporting:
Functional testing
API testing
UI automation and validation
Performance testing
Security testing support

QA Engineering & DevOps Integration
Integrate AI capabilities into CI/CD pipelines and quality engineering workflows
Collaborate with QA, Engineering, Product, and DevOps teams
Support enterprise-scale testing modernization initiatives
Promote AI-assisted development and testing methodologies

Leadership & Enablement
Mentor QA engineers and automation teams on AI-assisted testing practices
Lead proofs of concept, pilot programs, and enterprise rollouts
Establish standards and governance for AI adoption within QA organizations

Required Qualifications Education Bachelor's or Master's degree preferred in: Computer Science Engineering Data Science Related technical disciplines

Experience 10 years of overall industry experience Strong background in: o Software Testing o QA Automation o Quality Engineering o Test Architecture Minimum 3+ years of experience implementing: o Artificial Intelligence solutions o Machine Learning systems o Generative AI solutions o Enterprise AI initiatives

Note: Machine learning experience may contribute toward the AI requirement; however, candidates must demonstrate direct exposure to AI/GenAI technologies.

Required Technical Skills
AI / Machine Learning
Hands-on experience with:
Large Language Models (LLMs)
Natural Language Processing (NLP)
Machine Learning
Prompt Engineering
Enterprise AI implementations
AI-assisted workflows and automation

Programming Languages
Highest Priority
Java
SQL

Additional Preferred Languages
Python
JavaScript

Testing Tools & Frameworks Top Required Tools Selenium Playwright

Additional Preferred Tools
Karate
TestNG
API testing frameworks/tools
Tosca (preferred)

Quality Engineering & SDLC Knowledge
Strong understanding of:
SDLC (Software Development Life Cycle)
STLC (Software Testing Life Cycle)
QA operations and testing methodologies
Automation frameworks
CI/CD pipelines
Quality engineering best practices

Preferred Qualifications
Experience building AI copilots or AI-assisted QA solutions
Experience integrating AI into enterprise testing environments
Familiarity with:
o Retrieval-Augmented Generation (RAG)
o AI agents
o Workflow automation
o Observability and log analysis tools
o Scalable enterprise architecture design
Experience working in Agile/Lean environments
Knowledge of DevOps and modern engineering practices
Exposure to cloud platforms such as AWS, Azure, or Google Cloud Platform