Lead AI Quality Engineering

JOB DESCRIPTION
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:
  o Requirements analysis
  o Test case generation
  o Test automation development
  o Defect analysis
  o Traceability validation
  o Test data generation
  o Knowledge management
  o Documentation generation
  o 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

• Bachelors 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.