AI QA lead
Seneca Resources
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Job summary
Seneca Resources, a Fortune 500 client in New York, seeks an experienced AI Quality Engineering Lead to drive design, implementation, and enterprise adoption of AI-powered quality capabilities across its Testing Center of Excellence.
The role blends QE, software architecture, test automation, and enterprise AI, building reusable agents, frameworks, accelerators, and reference architectures while establishing governance, security, observability, and Human-in-the-Loop controls.
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.
Responsibilities
- AI-Powered Quality Engineering
- Agentic AI, LLM & RAG Engineering
- AI Governance, Responsible AI & Observability
Skills
Tools
Job description
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.
- AI-Powered Quality Engineering
- Agentic AI, LLM & RAG Engineering
- AI Governance, Responsible AI & Observability
- 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.
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