AI Quality Engineering Lead

Interview Requirement: Onsite Interview Required

We are seeking an experienced AI Quality Engineering Lead to drive the adoption of AI-powered Quality Engineering capabilities across an enterprise Testing Center of Excellence (TCoE). This hands-on technical leadership role will design, implement, and scale Agentic AI and GenAI solutions that improve software quality, automation, engineering productivity, and SDLC efficiency.

AI Quality Engineering Lead Responsibilities

• Lead enterprise adoption of AI-powered Quality Engineering capabilities across the SDLC.
• Define AI Quality Engineering strategy, architecture, roadmaps, standards, and governance.
• Design and implement Agentic AI solutions using LangGraph, LangChain, LLMs, RAG, and Multi-Agent architectures .
• Build reusable AI frameworks, accelerators, libraries, reference implementations, and engineering playbooks.
• Develop AI-enabled solutions for test case generation, test automation, defect analysis, traceability, test data generation, documentation, and quality analytics.
• Integrate AI solutions with DevOps and CI/CD pipelines .
• Establish Responsible AI, Human-in-the-Loop, AI observability, model evaluation, security, and governance standards.
• Define AI adoption metrics, KPIs, ROI, and value-realization frameworks.
• Provide technical leadership and mentoring while partnering with Engineering, Architecture, DevOps, Security, Product, and vendor teams.

AI Quality Engineering Lead Qualifications

Critical Requirements

• 8+ years of experience in Quality Engineering, Software Engineering, Test Automation, AI/ML, or enterprise technology delivery.
• 3+ years of experience designing and implementing AI/ML, GenAI, or Agentic AI solutions.
• Strong background in Test Consulting, Quality Engineering, and Test Automation .
• Hands-on experience with Python and FastAPI .
• Strong experience with LangGraph, LangChain, LLMs, RAG, Prompt Engineering, and Multi-Agent Systems .
• Experience designing scalable, enterprise-grade AI/ML solution architectures .
• Experience with Microservices, API-First Design, Event-Driven Architecture, Docker, Kubernetes, DevOps, and CI/CD .
• Experience building reusable AI/ML frameworks, accelerators, libraries, and reference implementations.
• Strong understanding of SDLC, AI governance, Responsible AI, security, and Human-in-the-Loop controls .
• Proven technical leadership, stakeholder management, consulting, and communication skills.
• Bachelor's degree or higher in Computer Science, Engineering, Information Systems, Data Science, AI, or a related field.

AI Quality Engineering Lead Preferred Qualifications

• Microsoft Azure AI, OpenAI, Azure AI Search , or other cloud-native AI platform experience.
• Enterprise Test Automation Framework development.
• AI observability, monitoring, and model evaluation experience.
• Engineering governance, operating models, and AI-first engineering practices.
• AI/ML, Cloud, or Architecture certifications.
• Experience leading enterprise engineering transformation and AI adoption initiatives.

The AI Quality Engineering Lead should combine deep Quality Engineering and Test Automation expertise with hands-on Agentic AI architecture and implementation experience. Candidates must be able to work onsite in New York City three days per week and attend an onsite interview .