QA Lead Tester

Peraton is seeking a Quality Assurance Lead Tester for the Department of Defense (DoD) DMDC Enterprise Law Enforcement Support (ELES2) program.  In this role, you will be responsible for developing and implementing testing strategies for AI/ML models. You will perform various types of testing, including but not limited to functional, performance, and scalability testing on AI/ML models. identify and document defects and verify fixes in AI/ML systems. You will be expected to design and implement a comprehensive testing framework, evaluating the performance of our privacy solutions in various ML contexts, and ensuring their reliability and consistency across different environments. You will be instrumental in integrating these testing processes into our continuous integration and deployment (CI/CD) pipeline, ensuring the highest standards of quality in our AI solutions.

 What You’ll Do:

• Identify and document defects and verify fixes in AI/ML systems.
• Deep understanding of different testing methodologies
• Experience with testing frameworks and tools
• Excellent problem-solving skills and attention to detail
• Strong verbal and written communication skills
• Proven experience in testing APIs and machine learning models (especially LLMs)
• Strong understanding of ML workflows
• Proficient in programming with Python

Required Qualifications:

• 5 years with BS/BA; 3 years with MS/MA; 0 years with PhD. 9 years of experience in lieu of degree
• Ability to obtain Public Trust clearance
• Must be a U.S Citizen
• Experience in leveraging AI-enabled testing tools

• Familiarity with Selenium AI/ML Layer Plugins uses such as:

  • Self-Healing Tests for automatically identifying and updating broken element locators (e.g., XPath/CSS changes) using visual recognition, DOM similarity, or historical test data.
  • Dynamic Element Identification to recognize UI elements even if their properties (ID, class) have changed.
  • Visual Testing to compare baseline screenshots using AI-driven image recognition to detect visual regressions.
  • Smart Test Generation to analyze user flows, code changes, or logs to suggest or auto-generate new test cases.
  • Impact Analysis to predict which tests need to be rerun based on code or UI changes, reducing test cycles

Desired Qualifications:

• Strong foundation in
  • software testing methodologies
  • test automation frameworks
  • defect tracking processes
• Proficiency with AI-enhanced platforms such as AI-enabled Selenium, which uses machine learning for adaptive test execution and element recognition
• AI-enabled Redwood, which supports self-healing test automation and intelligent error prediction
• Requires the ability to design and execute comprehensive test cases for functional, regression, and performance testing across web and enterprise applications.