AI QE Architect

Princeton IT Services, Inc; Charlotte (NC); On-site; USD 120,000 - 160,000; Part time

Job summary

Princeton IT Services, Inc is seeking an AI QE Architect in Charlotte, NC to lead the transition to AI-driven Quality Engineering. You will evaluate the existing QE landscape, implement AI solutions, and modernize testing practices through advanced technologies like Claude Code. This role demands 10-14 years of experience, strong leadership skills, and expertise in automation tools such as Playwright and Selenium. Join us to shape the future of quality engineering with innovative AI capabilities.

Qualifications

• 10-14 years' experience in Quality Engineering or Software Development in Test.
• Proven experience with Agentic AI and GenAI frameworks.
• Specific knowledge of Claude Code in development/testing workflows.

Responsibilities

• Lead AI-driven QE solutions and transformation roadmaps.
• Design autonomous test generation and self-healing automation frameworks.
• Collaborate with stakeholders to communicate outcomes and progress.

Skills

Quality Engineering expertise

AI & GenAI frameworks knowledge

Leadership skills

Strong API testing skills

Cloud-native architectures understanding

DevOps and CI/CD experience

Tools

Playwright

Selenium

Cypress

Claude Code

GitHub Actions

Job description

Job Title: AI QE Architect

Location: Charlotte, North Carolina (onsite)

Employment Type: contract

Job Description

We are seeking an AI QE Architect to serve as a dedicated AI Change Agent for our customer. In this role, you will lead the transition from traditional Quality Engineering to an AI-augmented ecosystem. You will be responsible for defining the strategy, building high-impact AI use cases, and modernizing the QE landscape using Generative AI and Agentic frameworks.

A critical component of this role is deep familiarity with Claude Code, as it is a core tool within the customer s existing environment.

Core Responsibilities

• Strategy & Assessment: Evaluate the current QE landscape (tools, frameworks, processes, and team maturity). Define and drive a comprehensive Agentic AI-led QE transformation roadmap.
• AI Implementation: Design and implement hands‑on AI‑driven QE solutions, including:
  • Autonomous Test Generation: Creating test cases and scripts using LLMs.
  • Self‑Healing Automation: Building frameworks that automatically adapt to UI/code changes.
  • Intelligent Analytics: Developing defect prediction models and automated triaging systems.
  • Synthetic Data: Implementing AI‑driven test data generation.
  • Ecosystem Modernization: Integrate AI capabilities into existing CI/CD pipelines and DevOps workflows to accelerate delivery.
  • Tooling & R&D: Evaluate next‑gen QE platforms, build Proof of Concepts (POCs), and develop reusable accelerators for scalable adoption across the enterprise.
• Leadership (Player‑Coach): Act as a hands‑on technical leader who can both architect high‑level strategy and contribute directly to code and implementation.
• Stakeholder Management: Collaborate with business, product, and engineering leadership to communicate progress, outcomes, and the value of AI initiatives.

Technical Skills & Qualifications

• Foundational Experience: 10 14 years of experience in Quality Engineering or Software Development in Test (SDET), with a track record of leading enterprise‑scale transformations.
• AI & GenAI Expertise: Proven experience with Agentic AI and GenAI frameworks (e.g., LangChain, CrewAI, AutoGen, or Cursor).
• Specific knowledge of Claude Code and its application in development/testing workflows.
• Deep understanding of LLMs and multi‑agent systems applied to QE.
• Core QE Proficiency: Expertise in modern automation tools like Playwright, Selenium, or Cypress.
• Strong grasp of API testing, microservices, and cloud‑native architectures.
• DevOps & Cloud: Hands‑on experience with GitHub Actions and CI/CD integration.
• Familiarity with cloud platforms (AWS, Azure, or GCP) in the context of AI and testing.
• Execution: Ability to build POCs from scratch and scale them into production‑ready frameworks.
• Communication: Exceptional ability to explain complex AI concepts to non‑technical stakeholders and senior leadership.