Job Title: Senior AI QA Automation Engineer
Location: Charlotte, NC and Iselin NJ
Job Summary
We are seeking a Senior / Lead AI QA Automation Engineer to drive the next generation of AI-powered test automation using Generative AI and Model Context Protocol (MCP) based solutions. This role will focus on building intelligent systems that ingest user stories, acceptance criteria, and test context from tools such as JIRA, Confluence, APIs, and code repositories, and automatically generate, execute, and maintain UI and API automation.
The ideal candidate will design context-aware, self-healing automation ecosystems using LLMs and MCP to reduce manual effort, improve reliability, and enable true shift-left quality engineering across agile teams.
Key Responsibilities
AI & MCP-Based Test Generation
- Design and implement MCP-based AI solutions that provide LLMs structured access to:
- User stories and acceptance criteria (JIRA/Confluence)
- Existing test assets and automation frameworks
- Application metadata, APIs, and UI locators
- Use LLMs (e.g., GPT-4, Claude) with MCP to automatically generate:
- BDD test scenarios (Gherkin)
- Executable UI and API automation scripts
- Test data and edge-case coverage
- Ensure generated tests remain context-aware, explainable, and traceable to requirements.
Intelligent Automation Framework Development
- Build and enhance AI-driven automation frameworks incorporating:
- MCP-based context retrieval for dynamic test generation
- Self-healing locators and adaptive test flows
- Automatic test refactoring when application or requirements change
- Establish standards for scalable, reusable, and maintainable automation.
Shift-Left & Agile Quality Enablement
- Enable test creation during story grooming and development sprints using AI-assisted workflows.
- Provide immediate feedback to developers via MCP-powered test execution and analysis.
- Collaborate closely with product owners, developers, and platform teams.
AI Application & Model Validation
- Validate AI-enabled features and systems, including:
- Prompt behavior and response quality
- MCP context accuracy and relevance
- RAG pipelines and tool-based grounding
- Non-deterministic outputs and hallucination risks
- Define validation strategies, scoring thresholds, and acceptance criteria for AI-driven functionality.
UI & API Automation
- Develop robust UI automation using Playwright (preferred), Selenium, or Cypress.
- Create comprehensive API automation for RESTful services and backend integrations.
- Ensure consistency between AI-generated tests and hand-crafted regression suites.
Flaky Test Detection & Optimization
- Leverage AI and MCP context to:
- Identify flaky tests and root causes
- Correlate failures with application, environment, or data changes
- Automatically recommend or implement fixes
- Improve overall test suite stability and reliability.
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