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# AI Software Test Engineer

- **Company:** Aquent
- **Location:** Ann Arbor, MI, United States
- **Salary:** $135,000.0 - $180,000.0
- **Contract:** Permanent contract
- **Skills:** Testing (Software), Java (Programming Language), JavaScript (Programming Language), Artificial Intelligence, Automation of Tests, Continuous Integration, Python (Programming Language), Load Testing, Machine Learning, Regression Testing, Selenium, Software Systems, Strategies of Testing, Test Scripts, Git, Integration Tests, Playwright, Mocha, Api Management, Jenkins
- **Published:** October 5, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=3005093505-2

## About the Role

* Software testing
 * Test automation
 * Python
 * Java
 * Script or Type
 * Script
 * Selenium or Playwright
 * Py
 * Test or Jest or Mocha
 * API testing (REST/Graph
 * QL)
 * CI/CD tools (Jenkins, Git
 * Hub Actions, Git
 * Lab CI)
 * AI/ML model validation
 * Performance and load testing

## Description

Aquent Talent is seeking an AI Software Test Engineer to ensure the quality and reliability of innovative AI-driven applications for leading creative and digital clients. In this role, you'll design and execute automated and manual tests, validate AI models, and build robust test frameworks that integrate into modern CI/CD pipelines. You'll partner closely with engineers, data scientists, and product teams in a people-first, inclusive culture that values experimentation and continuous learning. This is a great opportunity to shape how AI is tested, improve product outcomes, and grow your expertise while collaborating with top global brands., * Design and execute automated and manual tests for AI-driven software solutions across web and cloud platforms

 * Develop, maintain, and optimize test frameworks, test scripts, and CI/CD-integrated test suites
 * Validate AI models for accuracy, performance, bias, and reliability; create reproducible test datasets
 * Collaborate with developers, data scientists, and product teams to define test strategies and acceptance criteria
 * Analyze defects, triage issues, and drive root-cause investigations with engineering teams
 * Implement performance, load, and regression testing for scalable AI applications
 * Document test plans, test cases, and test results with clear, actionable reporting
 * Contribute to quality standards, best practices, and continuous improvement of QA processes
 * Support release readiness with risk assessments and go/no-go quality recommendations
 * Stay current on AI testing tools, frameworks, and industry QA best practices