AI Quality Engineer #1

Ariel Partners•Nyc, NY

18h•Onsite

About The Position

SNAP Payment Error Rate (CAP) reduction initiative is a top-priority, agency-wide strategic effort aimed at mitigating federal oversight findings and avoiding substantial financial penalties amounting to millions of dollars. In partnership with McKinsey, the agency is leveraging artificial intelligence, Robotic Process Automation and advanced analytics to strengthen eligibility, case processing accuracy, and quality control review. This initiative will modernize error detection, introduce proactive prevention capabilities, and enhance operational decision-making through data-driven insights. The outcome is expected to reduce payment inaccuracies, accelerate case resolution, improve compliance, and increase public trust in program integrity.

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