AI Quality Engineering Lead (Hybrid - Onsite interviews)

Job title: AI Quality Engineering Lead

Job Location - NY, NY

Duration: 12+ months

OPEN TO GC/USC/GC EAD/H4 EAD/H1B (they must be local)

Interview: In-person

Hybrid expectation 3 days onsite per week

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AI Quality Engineering Lead | Agentic AI, Enterprise AI Solutions, AI-Driven Software Quality Engineering

Role Overview

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-first engineering practices.
• Experience in technical consulting and stakeholder management.
• Experience with Microsoft Azure AI, OpenAI, Azure AI Search and cloud-native AI platforms.
• Experience leading engineering transformation and AI adoption initiatives.

Required Experience

• 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.
• Proven experience leading enterprise-scale technical initiatives and cross-functional teams.
• Experience defining architecture standards, governance frameworks, and reusable engineering solutions.

Education and Qualifications

Bachelor's Degree or higher in Computer Science, Engineering, Information Systems, Data Science, Artificial Intelligence, or a related field.

Advanced AI/ML, Cloud, or Architecture certifications preferred.

Strong software engineering and solution architecture background preferred.

What Success Looks Like

• AI-powered Quality Engineering solutions are successfully adopted across TCoE programs and delivery teams.
• Reusable AI agents, frameworks, accelerators, and reference architectures are established and broadly utilized across the organization.
• Measurable improvements are achieved in testing productivity, automation efficiency, software quality, and delivery velocity.
• Responsible AI, security, governance, observability, and Human-in-the-Loop controls are consistently implemented.
• Leadership has clear visibility into AI adoption, business value, risk management, and ROI.

“Cleo Consulting is an equal opportunity employer (Minorities/Women/Veterans/Disabled)”