AI Quality Engineering Lead - Hybrid NYC

Expert Technology Services; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

Expert Technology Services is seeking an AI Quality Engineering Lead to drive enterprise adoption of AI-powered quality engineering across the SDLC and Testing Center of Excellence. You will define strategies, roadmaps, and governance models to elevate software quality and automation.

You will design scalable AI solutions, build reusable frameworks, lead teams, and collaborate across engineering, DevOps, security, and product groups.

Qualifications

• 8+ years in Quality Engineering, Software Engineering, Test Automation, AI/ML, or Enterprise Technology Delivery.
• 3+ years in designing and implementing AI/ML, GenAI, or Agentic AI solutions.
• Experience with Python, FastAPI, LangChain, LangGraph, LLMs, RAG, and AI orchestration frameworks.
• Proven expertise in building reusable frameworks, accelerators, and reference implementations for AI/ML-augmented engineering.
• Strong background in SDLC, software architecture, DevOps, CI/CD, microservices, API-first design, and event-driven architecture.
• Experience establishing governance, Responsible AI, and Human-in-the-Loop controls.
• Excellent technical leadership, stakeholder management, and communication skills.

Responsibilities

• Lead the enterprise adoption of AI-powered Quality Engineering solutions across the Software Development Lifecycle (SDLC) and Testing Center of Excellence (TCoE).
• Define and execute strategies, roadmaps, standards, and governance models for AI Quality Engineering.
• Design, implement, and scale enterprise AI solutions to improve software quality, engineering productivity, automation, and SDLC efficiency.
• Develop and maintain reusable AI frameworks, accelerators, libraries, reference implementations, and engineering playbooks.
• Drive the implementation of AI-enabled solutions in requirements analysis, test case generation, test automation, defect analysis, traceability validation, test data generation, knowledge management, documentation, and quality analytics.
• Establish and enforce standards for Responsible AI, Human-in-the-Loop controls, AI observability, model evaluation, security, and governance.
• Integrate AI solutions into DevOps and CI/CD pipelines to accelerate software delivery and maintain high quality.
• Evaluate emerging AI technologies and recommend adoption strategies for the enterprise.
• Define metrics, KPIs, ROI measures, and value realization frameworks for AI adoption.
• Provide technical leadership, mentorship, and support to engineering teams in adopting AI-first delivery practices.
• Collaborate with engineering, architecture, DevOps, security, product teams, and vendor partners to ensure seamless adoption of AI solutions.
• Ensure compliance with software engineering best practices, security guidelines, and Responsible AI principles.
• Lead the development and scaling of Agentic AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Multi-Agent Systems within quality engineering.
• Support continuous improvement through feedback, monitoring, and operational excellence in AI-powered quality engineering initiatives.
• Report on AI adoption progress, business value, risk management, and ROI to senior leadership.

Skills

Quality Engineering

Software Engineering

Test Automation

AI/ML

GenAI / Agentic AI

AI orchestration frameworks

Python

DevOps / CI/CD

SDLC

Governance / Responsible AI

Stakeholder management

Education

Bachelor's degree or higher in CS/Engineering/Data Science/AI

Advanced certifications in AI/ML, Cloud, or Architecture

Tools

Python

FastAPI

LangChain

LangGraph

LLMs

RAG

Job description

Job Summary for AI Quality Engineering Lead

• Lead the enterprise adoption of AI-powered Quality Engineering solutions across the Software Development Lifecycle (SDLC) and Testing Center of Excellence (TCoE).
• Define and execute strategies, roadmaps, standards, and governance models for AI Quality Engineering.
• Design, implement, and scale enterprise AI solutions to improve software quality, engineering productivity, automation, and SDLC efficiency.
• Develop and maintain reusable AI frameworks, accelerators, libraries, reference implementations, and engineering playbooks.
• Drive the implementation of AI-enabled solutions in requirements analysis, test case generation, test automation, defect analysis, traceability validation, test data generation, knowledge management, documentation, and quality analytics.
• Establish and enforce standards for Responsible AI, Human-in-the-Loop controls, AI observability, model evaluation, security, and governance.
• Integrate AI solutions into DevOps and CI/CD pipelines to accelerate software delivery and maintain high quality.
• Evaluate emerging AI technologies and recommend adoption strategies for the enterprise.
• Define metrics, KPIs, ROI measures, and value realization frameworks for AI adoption.
• Provide technical leadership, mentorship, and support to engineering teams in adopting AI-first delivery practices.
• Collaborate with engineering, architecture, DevOps, security, product teams, and vendor partners to ensure seamless adoption of AI solutions.
• Ensure compliance with software engineering best practices, security guidelines, and Responsible AI principles.
• Lead the development and scaling of Agentic AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Multi-Agent Systems within quality engineering.
• Support continuous improvement through feedback, monitoring, and operational excellence in AI-powered quality engineering initiatives.
• Report on AI adoption progress, business value, risk management, and ROI to senior leadership.

Required Skills & Experience

• 8+ years in Quality Engineering, Software Engineering, Test Automation, AI/ML, or Enterprise Technology Delivery.
• 3+ years in designing and implementing AI/ML, GenAI, or Agentic AI solutions.
• Experience with Python, FastAPI, LangChain, LangGraph, LLMs, RAG, and AI orchestration frameworks.
• Proven expertise in building reusable frameworks, accelerators, and reference implementations for AI/ML-augmented engineering.
• Strong background in SDLC, software architecture, DevOps, CI/CD, microservices, API-first design, and event-driven architecture.
• Experience establishing governance, Responsible AI, and Human-in-the-Loop controls.
• Excellent technical leadership, stakeholder management, and communication skills.

Preferred Skills & Experience

• Experience with Microsoft Azure AI, OpenAI, Azure AI Search, and cloud-native AI platforms.
• Track record in building enterprise test automation frameworks, AI observability, and monitoring solutions.
• Background in technical consulting and leading engineering transformation or AI adoption initiatives.

Education & Certifications

• Bachelor's degree or higher in Computer Science, Engineering, Data Science, AI, or related field.
• Advanced certifications in AI/ML, Cloud, or Architecture preferred.

Success Measures

• Broad adoption of AI-powered quality engineering solutions across TCoE and delivery teams.
• Measurable improvements in testing productivity, automation, software quality, and delivery speed.
• Consistent application of Responsible AI, security, governance, and observability controls.
• Clear visibility for leadership into AI adoption, business value, risk management, and ROI.