Company Description
Zayo provides mission-critical bandwidth to the world’s most impactful companies, fueling the innovations that are transforming our society. Zayo’s 141,000-mile network in North America and Europe includes extensive metro connectivity to thousands of buildings and data centers. Zayo’s communications infrastructure solutions include dark fiber, private data networks, wavelengths, Ethernet, and dedicated Internet access. Zayo serves wireless and wireline carriers, media, tech, content, finance, healthcare and other large enterprises.
We are seeking an experienced Director, Enterprise Quality Engineering and Agentic Evaluation to build and lead a modern enterprise quality capability across Zayo’s technology organization. This role will be accountable for improving the reliability, predictability, and speed of technology delivery while providing independent assurance for critical business platforms, transformational programs, and AI-enabled solutions.
The successful candidate will combine deep quality engineering expertise with strong enterprise leadership, bringing a risk-based, automation-first approach to how quality is embedded across the software delivery lifecycle. The role will also shape how Zayo evaluates and governs emerging agentic AI capabilities, ensuring they are reliable, controlled, and ready for enterprise-scale adoption.
This is a highly collaborative and transformational leadership role, partnering across Engineering, Product, Architecture, Data and AI, Operations, and business teams to strengthen quality outcomes, reduce production risk, and establish a culture of measurable engineering excellence.
Key Responsibilities
Define and lead Zayo’s enterprise quality engineering strategy, operating model, roadmap, service catalog, standards, and governance.
Own the enterprise quality engineering platform, including Tosca, qTest, automation assets, integrations, test execution infrastructure, quality reporting, and AI-evaluation capabilities.
Establish risk-based quality models, release-readiness criteria, quality gates, certification standards, and evidence requirements for critical platforms, business processes, integrations, data products, and AI agents.
Lead a centralized quality engineering service that plans, automates, executes, and governs testing across enterprise products, platforms, programs, and high-risk releases.
Develop reusable automation frameworks, model-based testing patterns, API and integration testing capabilities, regression suites, and continuous testing practices aligned to business risk and production impact.
Establish enterprise capabilities for AI-agent evaluation, including scenario design, evaluation datasets, regression testing, tool-use validation, policy and permission checks, human escalation criteria, and production quality monitoring.
Own the enterprise approach to test data, test environments, service virtualization, production validation, and quality analytics to reduce release risk and delivery delays.
Provide independent quality intelligence and release-risk assessments to engineering, product, program, and executive stakeholders.
Manage vendor-outsourced quality engineering resources, Tricentis partners, systems integrators, and specialist testing providers to deliver sustained enterprise quality outcomes.
Build a quality engineering culture focused on automation, objective assurance, reuse, engineering discipline, measurable outcomes, and continuous improvement.
Qualifications
Bachelor’s degree in Computer Science, Engineering, Information Systems, Mathematics, or a related discipline; equivalent relevant experience may be considered.
Minimum of twelve (12) years of experience in quality engineering, software testing, test automation, platform engineering, enterprise technology delivery, or related technology disciplines, including 5+ years leading quality engineering, test automation, enterprise testing, or platform-oriented teams.
Demonstrated experience…