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QA Automation Lead; AI expertise

Job in Brick, Ocean County, New Jersey, 08724, USA
Listing for: REDLEO SOFTWARE INC.
Full Time position
Listed on 2026-09-13
Job specializations:
  • Quality Assurance - QA/QC
    IT QA Tester / Automation, AI QA / Validation Engineer
Salary/Wage Range or Industry Benchmark: 120000 - 180000 USD Yearly USD 120000.00 180000.00 YEAR
Job Description & How to Apply Below
Position: QA Automation Lead (AI expertise)
  • Looking for a QA lead with 8+ years of experience in Quality Engineering, Test Automation, API/ UI testing CI/CD and AI-led testing
    , with strong technical leadership and the ability to defile and drive QA Strategy
  • Strong hands-on experience in API testing and automation using REST/SOAP services.
  • Expertise in UI automation testing using Selenium or equivalent frameworks.
  • Strong experience in functional, integration, regression, system, API, and end-to-end testing.
  • Experience with SQL and database validation, including MySQL/SQL Server or equivalent databases.
  • Strong understanding of JSON, XML, REST APIs, SOAP, web services, and microservices architectures.
  • Strong experience with cloud environments, preferably Pivotal Cloud Foundry and/or AWS/Azure/GCP
  • Strong experience in AI-led Quality Engineering, leveraging AI/GenAI capabilities to improve test planning, test design, automation, execution, defect analysis, and overall software quality.
  • Preferably generating test case and script creation using copilot.
  • Experience using AI-assisted test generation and automation tools to generate test scenarios, test cases, automation scripts, regression suites, and test data.
  • Experience leveraging AI/GenAI for defect analysis, root-cause analysis, log analysis, failure prediction, and intelligent test prioritization.
  • Experience evaluating and adopting AI-powered QA/testing tools to improve testing efficiency, coverage, productivity, and quality.
  • Hands-on experience with AI development/productivity tools such as Git Hub Copilot or equivalent AI tools
    , with the ability to apply them effectively in QA automation and Quality Engineering activities.
  • Ability to identify opportunities where AI can replace or augment repetitive manual QA activities and drive measurable improvements in automation and productivity.
  • Understanding of AI/ML application testing, including validation of AI-driven functionality, data quality, model behavior, accuracy, reliability, and edge cases, where applicable.
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