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.
location: New York, New York
job type: Contract
salary: $70 - 73 per hour
work hours: 9am to 5pm
education: Bachelors
responsibilities:
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.
- 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.
- 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.
- 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.
- 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.
- 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.
qualifications:
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.
skills:
AI platforms,API-First,reusable frameworks,AI solutions,AI,AI frameworks,AI-enabled solutions,integration of AI,emerging AI technologies,AI-enabled,Artificial Intelligence,Advanced AI,Test automation,cloud infrastructure,Cloud,cloud-native,CI/CD pipelines,data generation,data pipelines,DevOps,DevOps practices,Docker,Enterprise Technology,Event-Driven Architecture,FastAPI,Retrieval-Augmented Generation (RAG),Information Systems,Computer Science,Knowledge management,Kubernetes,Large Language Models,LLMs,Machine Learning models,Microservices,Microsoft Azure,Azure,model evaluation,Multi-Agent Systems,Multi-Agent,AI orchestration,Prompt Engineering,Python,release management,AI Search,Software Architecture,Software Development Lifecycle,Software Engineering,software quality,Test Automation Frameworks,SDLC,Test case,Agentic AI,communication skills,leadership,Architecture,architecture standards,automation,automated,software engineering,software quality,Consulting,Responsible AI,Data Science,validation Test,Defect analysis,governance,governance frameworks,AI governance,AI systems,mentoring,metrics,operating model,operating models,automation efficiency,Quality reporting,Quality Engineering,reference architectures,Requirements analysis,risk management,Security,Solution Architecture design,Solution Architecture,stakeholder management,technical consulting,technical leadership,testing,Traceability,business value
Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.
At Randstad Digital, we welcome people of all abilities and want to ensure that our hiring and interview process meets the needs of all applicants. If you require a reasonable accommodation to make your application or interview experience a great one, please contact HRsupport@randstadusa.com.
Pay offered to a successful candidate will be based on several factors including the candidate's education, work experience, work location, specific job duties, certifications, etc. In addition, Randstad Digital offers a comprehensive benefits package, including: medical, prescription, dental, vision, AD&D, and life insurance offerings, short-term disability, and a 401K plan (all benefits are based on eligibility).
This posting is open for thirty (30) days.
