AI Quality Engineering Lead

Location:

4 Irving Place, New York, NY (Hybrid: 3 Days Onsite / 2 Days Remote)

AI Quality Engineering Lead

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

Role Overview

We are seeking a highly motivatedAI Quality Engineering Lead with8+ years of experience in Quality Engineering, Test Automation, Software Engineering, AI/ML, and Technology Transformation to lead the adoption ofAI-powered Quality Engineering capabilities across the Testing Center of Excellence (TCoE).

This is ahands-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 ofAgentic 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 throughAI-first engineering practices while maintaining quality, security, and Responsible AI standards.

Key Responsibilities

• Lead enterprise adoption ofAI-powered Quality Engineering capabilities across the SDLC.
• Define and execute theAI Quality Engineering strategy, roadmap, standards, and governance model.
• Design and implementAgentic AI solutions using LangChain, LangGraph, LLMs, RAG, and Multi-Agent architectures.
• Develop reusableAI 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 forResponsible AI, Human-in-the-Loop controls, AI observability, model evaluation, security, and governance.
• Drive integration of AI solutions intoDevOps and CI/CD pipelines.
• Evaluate emerging AI technologies and establish enterprise adoption recommendations.
• DefineAI 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 inTest Consulting, Quality Engineering, and Test Automation.
• Experience inAI/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 inPython,FastAPI framework
• Experience inAgentic AI engineering workflow orchestration using LangGraph, LangChain, Large Language Models (LLMs), and AI orchestration frameworks.
• Experience in building reusablereference implementations, libraries, accelerators, frameworks, and playbooks for AI/ML-augmented engineering and software delivery.
• Experience inPrompt Engineering, Solution Architecture and Design, Retrieval-Augmented Generation (RAG), and Multi-Agent Systems.
• Experience withMicroservices, API-First Design, and Event-Driven Architecture.
• Experience withDocker, Kubernetes, DevOps practices, and CI/CD pipelines.
• Experience inSoftware Architecture, Engineering Transformation, and AI-driven Engineering Solutions.
• Strong understanding ofSoftware Development Lifecycle (SDLC), Quality Engineering, and AI-enabled software delivery practices.
• Experience establishingAI 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 enterpriseTest Automation Frameworks and reusable automation accelerators.
• Experience inAI observability, monitoring, model evaluation, and operational monitoring frameworks.
• Experience inautomated documentation generation and release management solutions.
• Experience inengineering governance, standards, operating models, and AI-first engineering practices.
• Experience intechnical consulting and stakeholder management.
• Experience withMicrosoft Azure AI, OpenAI, Azure AI Search and cloud-native AI platforms.
• Experience leadingengineering 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.
• AdvancedAI/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 acrossTCoE programs and delivery teams.
• ReusableAI agents, frameworks, accelerators, and reference architectures are established and broadly utilized across the organization.
• Measurable improvements are achieved intesting 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 intoAI adoption, business value, risk management, and ROI.