AI Quality Engineer

G2W Consulting•Brooklyn, NY

1d•Hybrid

About The Position

We are hiring for a senior QA Automation Engineer specializing in AI-driven testing and intelligent automation. The position focuses on designing comprehensive AI agent test scenarios, conducting performance and API testing, integrating Selenium AI plugins and agentic tools, and embedding automated testing into CI/CD pipelines—while leveraging strong experience in Agile, defect lifecycle management, and enterprise automation frameworks to improve quality, scalability, and process efficiency. This is an ongoing contract, hybrid role (three days onsite/ two days remote).

Requirements

Minimum 5 years of experience with industry-standard tools such as JIRA, AZDO, Balsamiq, and MS Visio.
Minimum 5 years of experience with automation tools including QTP, WinRunner, Visual Studio, and Selenium.
Minimum 5 years of experience in creating, executing, and managing automation scripts effectively.
Minimum 5 years of experience in utilizing SQL Server, LoadRunner, and JMeter for database management and performance testing.
Minimum 5 years of experience in exhibiting strong understanding and practical experience with Agile and Scrum methodologies.
Minimum 5 years of experience in using version control systems like Git and platforms such as GitHub or GitLab for collaborative development and test management.
Minimum 5 years of experience in exhibiting proficiency in end-to-end defect life cycle management, including logging, tracking, and verifying fixes.
Minimum 5 years of experience in integrating automated tests into Continuous Integration/Continuous Deployment (CI/CD) pipelines using tools like Jenkins or Azure DevOps.

Responsibilities

AI Agent Test Scenario Creation: Designing robust and relevant test scenarios to validate AI agent behavior and performance.
PRD Analysis: Analyzing Product Requirement Documents (PRDs) to extract testing requirements and ensure comprehensive test coverage.
Performance Testing: Conducting thorough performance testing of AI systems to identify bottlenecks and ensure scalability.
Agentic Tool: Utilizing agentic tools such as Windsurf, Claude Code, and Cursor for advanced automation tasks.
Selenium AI Plugins: Implementing and leveraging AI-powered Selenium plugins, including Healenium and Applitools Eyes, for intelligent visual and functional testing.
QA Automation with MCP Integration: Integrating QA automation processes with our Master Control Program (MCP) for centralized management and execution.
APIs & Integrations: Testing and integrating with REST and SOAP APIs to ensure seamless connectivity between systems.
Process Analysis Workflow Analysis: Analyzing complex business workflows to understand processes and identify pain points.
Identifying Automation Opportunities: Proactively identifying areas within the QA lifecycle and business processes that can be optimized through AI automation.
Excel Automation: Automating data-intensive tasks using Excel automation techniques