AI Quality Engineer Lead - Hybrid NYC

Job Summary We are seeking a highly motivated AI Quality Engineering Lead with 8+ years of experience in Quality Engineering, Test Automation, Software Engineering, AI/ML, and Technology Transformation to lead the adoption of AI-powered Quality Engineering capabilities across the Testing Center of Excellence (TCoE).

This is a hands-on technical leadership role responsible for designing, implementing, and scaling enterprise AI solutions that improve software quality, engineering productivity, automation, and SDLC efficiency. The role will drive adoption of Agentic AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Multi-Agent Systems, AI-powered testing solutions, and engineering accelerators while establishing governance, standards, and reusable frameworks for enterprise use.

The AI Quality Engineering Lead will partner closely with Engineering, Architecture, DevOps, Security, Product Teams, and Vendor Partners to accelerate software delivery through AI-first engineering practices while maintaining quality, security, and Responsible AI standards.

Key Responsibilities

Lead enterprise adoption of AI-powered Quality Engineering capabilities across the SDLC.

Define and execute the AI Quality Engineering strategy, roadmap, standards, and governance model.

Design and implement Agentic AI solutions using LangChain, LangGraph, LLMs, RAG, and Multi-Agent architectures.

Develop reusable AI frameworks, accelerators, libraries, reference implementations, and engineering playbooks.

Lead Implementation Of AI-enabled Solutions For Requirements analysis

Test case generation

Test automation development

Defect analysis

Traceability validation

Test data generation

Knowledge management

Documentation generation

Quality reporting and analytics

Establish standards for Responsible AI, Human-in-the-Loop controls, AI observability, model evaluation, security, and governance.

Drive integration of AI solutions into DevOps and CI/CD pipelines.

Evaluate emerging AI technologies and establish enterprise adoption recommendations.

Define AI adoption metrics, KPIs, ROI measures, and value realization frameworks.

Provide technical leadership and mentoring to engineering teams adopting AI-first delivery practices.

Collaborate with senior leadership to define and evolve the enterprise AI-enabled Quality Engineering operating model.

Required Skills & Experience Experience in Test Consulting, Quality Engineering, and Test Automation.

Experience in AI/ML Solution Architecture design to create scalable, enterprise-grade AI systems by selecting optimal models (e.g., LLMs and traditional Machine Learning models), defining data pipelines, and ensuring seamless integration with existing cloud infrastructure and governance frameworks.

Experience in Python, FastAPI framework

Experience in Agentic AI engineering workflow orchestration using LangGraph, LangChain, Large Language Models (LLMs), and AI orchestration frameworks.

Experience in building reusable reference implementations, libraries, accelerators, frameworks, and playbooks for AI/ML-augmented engineering and software delivery.

Experience in Prompt Engineering, Solution Architecture and Design, Retrieval-Augmented Generation (RAG), and Multi-Agent Systems.

Experience with Microservices, API-First Design, and Event-Driven Architecture.

Experience with Docker, Kubernetes, DevOps practices, and CI/CD pipelines.

Experience in Software Architecture, Engineering Transformation, and AI-driven Engineering Solutions.

Strong understanding of Software Development Lifecycle (SDLC), Quality Engineering, and AI-enabled software delivery practices.

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

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

Preferred Skills & Experience Experience building enterprise Test Automation Frameworks and reusable automation accelerators.

Experience in AI observability, monitoring, model evaluation, and operational monitoring frameworks.

Experience in automated documentation generation and release management solutions.

Experience in engineering governance, standards, operating models, and AI-f…