AI QA lead
seneca resourcesNew York (NY)
About the role
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), Lang Chain, Lang Graph, 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 EngineeringAI/ML Solution ArchitectureAI Governance, Responsible AI & Observability Dev Ops, 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 Lang Graph, Lang Chain, 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, Dev Ops, 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.
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