QA Automation Engineering

QA Automation Engineer
Greenberg Traurig (GT), a global law firm with locations across the world in 15 countries, has an exciting employment opportunity for you. Join our Technology Team as a QA Automation Engineer located in various offices.
If you are someone who demonstrates initiative, adaptability, and innovation, we invite you to join our team.
This role reports to the Quality Assurance Manager.
The QA Automation Engineer designs, develops, and maintains automated test solutions that improve software quality, reduce manual effort, and support reliable releases. This role partners with QA, development, business analysis, project management, and DevOps teams to build scalable test coverage across functional, regression, integration, and API testing. The role also uses AI-assisted tools to accelerate test design, execution, maintenance, and analysis. The ideal candidate combines strong automation skills with analytical thinking, attention to detail, sound judgment, and a continuous improvement mindset to use AI responsibly and effectively throughout the software quality lifecycle.
Designs, develops, and maintains automated test frameworks and reusable scripts for web, API, and integrated systems
Reviews business and technical requirements to identify test scenarios, acceptance criteria, automation opportunities, and where AI-assisted design can improve speed and coverage
Uses AI-assisted tools to create and maintain test cases, data, and scripts, with appropriate review for accuracy and coverage
Executes regression, functional, smoke, integration, and API tests to validate quality and release readiness
Tests AI-powered applications and agents across prompts, scenarios, and edge cases to confirm accuracy, reliability, consistency, and expected behavior
Evaluates AI-generated outputs against expected results and identifies inaccuracies, inconsistencies, or misleading responses
Analyzes test results, investigates failures, documents defects, and uses AI-enabled insights to speed triage, root cause analysis, and retesting
Integrates automated tests into CI/CD pipelines to provide fast, reliable feedback during builds and deployments
Maintains test data, environments, automation assets, and AI-generated artifacts to support stable, repeatable, and auditable execution
Tracks quality metrics such as coverage, defect trends, pass/fail rates, automation effectiveness, and the impact of AI-assisted testing
Ensures AI use in testing complies with governance, data protection, and responsible AI standards
Continuously improves automation frameworks, QA processes, and supporting tools to increase efficiency, governance, and overall effectiveness
Performs other duties as assigned by the QA Manager
Working knowledge of API testing, test data management, defect tracking, version control, and CI/CD practices
Proficiency in one or more programming or scripting languages.
Understanding of the software development lifecycle, Agile methodologies, and quality assurance best practices
Knowledge of performance, security, accessibility, or risk-based testing practices preferred
Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent practical experience
Experience in software quality assurance, including hands-on test automation development
Experience with test automation tools and frameworks
Experience integrating automated tests into CI/CD tools such as Azure DevOps, Jenkins, GitHub Actions, or similar platforms preferred
Experience using generative AI or intelligent assistants to support test design, documentation, defect triage, or automation maintenance preferred
Experience testing enterprise applications, cloud-based platforms, or complex multi-system workflows preferred
Automation & Testing: UI and API automation, regression, smoke, and integration testing, test framework design, defect management, and AI-assisted test maintenance
Tools & Platforms: Selenium, Azure DevOps, Jira, and similar automation and delivery tools
Quality Engineering Practices: Prompting for test design, review of AI-generated artifacts, test data generation, CI/CD, risk-based testing, root cause analysis, test reporting, governance, and continuous improvement