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.