What are the responsibilities and job description for the AI QE Architect position at Atinfo Technology Inc?
Onsite from Day one. Full time role.
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.
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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 Google Cloud Platform) 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.