AI QA lead

Our Fortune 500 client located in New York, NY is seeking an experienced AI Quality Engineering Lead - Agentic AI & GenAI to lead the design, implementation, and enterprise adoption of AI-powered Quality Engineering capabilities across its Testing Center of Excellence (TCoE).

This is a hands-on technical leadership role at the intersection of Quality Engineering, software architecture, test automation, and enterprise AI. The successful candidate will design and build production-ready solutions utilizing Agentic AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LangChain, LangGraph, and Multi-Agent architectures to transform software testing and engineering practices across the SDLC.

The ideal candidate combines a strong Quality Engineering and test automation foundation with hands-on AI/ML and GenAI engineering expertise. This individual will build reusable AI agents, frameworks, accelerators, libraries, and reference architectures while establishing the governance, security, observability, evaluation, and Human-in-the-Loop controls necessary to scale AI responsibly across enterprise engineering teams.

Key Responsibilities

AI-Powered Quality Engineering

Agentic AI, LLM & RAG Engineering

AI/ML Solution Architecture

AI Governance, Responsible AI & Observability

DevOps, CI/CD & Cloud Integration

Required Qualifications

8+ years of experience across Quality Engineering, Software Engineering, Test Automation, AI/ML, or enterprise technology delivery.

3+ years of hands-on experience designing and implementing AI/ML, Generative AI, or Agentic AI solutions.

Strong background in Quality Engineering, test automation, and software testing practices.

Hands-on experience with Python and FastAPI.

Strong experience with Agentic AI and workflow orchestration using LangGraph, LangChain, LLMs, and related AI frameworks.

Hands-on experience designing and implementing Retrieval-Augmented Generation (RAG) solutions.

Experience designing Multi-Agent Systems and AI orchestration architectures.

Strong prompt-engineering experience.

Experience with AI/ML solution architecture, including model selection, data pipelines, integrations, scalability, security, and governance.

Experience developing reusable AI frameworks, libraries, accelerators, agents, reference implementations, and engineering playbooks.

Strong understanding of microservices, API-first design, and event-driven architecture.

Hands-on experience with Docker, Kubernetes, DevOps, and CI/CD pipelines.

Strong understanding of SDLC, Quality Engineering, test automation, and AI-enabled software-delivery practices.

Experience establishing Responsible AI, AI governance, Human-in-the-Loop controls, security standards, and engineering best practices.

Proven experience leading enterprise-scale technical initiatives and cross-functional engineering teams.

Strong technical leadership, consulting, stakeholder-management, presentation, and communication skills.