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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.
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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 building or leading modern quality engineering capabilities beyond manual test execution, including automation strategy, risk-based testing, continuous testing, quality gates, and release assurance.
- Strong experience with enterprise test automation and test management platforms, preferably Tricentis Tosca and qTest.
- Experience leading testing or quality engineering across multiple products, platforms, business processes, programs, or business units.
- Strong understanding of API, integration, data, workflow, cloud, event-driven, distributed-system, performance, resilience, and production validation testing.
- Practical knowledge of generative AI, large language models, retrieval, AI agents, tool calling, evaluation methods, and responsible AI testing practices.
Strong executive communication, stakeholder management, vendor management, financial management, organizational leadership, and decision-making skills.
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Estimated base salary range: $126,600 - $194,700 USD/annually.
Work Authorization Requirement: This position is not eligible for employment-based visa sponsorship. Zayo will not provide, transfer, or assume sponsorship for any employment visa or work authorization for this position now or in the future.
#LI-Remote
The base pay range shown is a guideline and reasonable estimate for this role. It takes into account the wide variety of factors that are considered in making compensation decisions. Actual compensation offered may vary from the posted range based upon geographic location, work experience, skill level, certifications, and other business and organizational needs. Non- sales roles may be eligible to participate in a discretionary annual incentive plan. Sales roles may be eligible to participate in a sales incentive plan.
Additionally, this position may be eligible for certain benefits, such as health insurance, life insurance, disability retirement plans, paid time off.
The posting will be active for a minimum of 3 days. The active posting will continue to extend by 3 days until the position is filled.
Benefits, Rewards & Wellness
- Excellent Health, Dental & Vision Insurance
- Retirement 401(k) Savings Plan
Generous paid time off policy including paid parental leave *
Zayo is an equal opportunity employer to all protected groups, including protected veterans and individuals with disabilities. If you are an individual with a disability and would like to request a reasonable accommodation as part of the employment selection process, please contact us.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.
As part of the application, hiring, onboarding, and/or employment process, Zayo or its affiliates, partners, and vendors may collect, store, and use biometric identifiers and biometric information (collectively, “Biometric Data”), such as fingerprints, facial geometry scans or other unique biological characteristics. Zayo uses CLEAR1, a third-party identity verification service, to verify applicant identity during the hiring process. CLEAR1 may collect Biometric Data, including facial geometry scans and/or other biometric identifiers, to authenticate your identity. Biometric Data will be retained pursuant to CLEAR1’s policies, which can be found here: https://www.clearme.com/clear1-credential-policy-crp. Zayo will not sell, lease, trade, or otherwise profit from any applicant's or employee's Biometric Data. By proceeding with your application, you acknowledge this disclosure. Prior to any collection of Biometric Data — including through CLEAR — you may be presented with a separate consent form and asked to provide your written authorization in accordance with applicable federal, state, and local laws, including the Illinois Biometric Information Privacy Act (BIPA), the Texas Capture or Use of Biometric Identifier Act (CUBI), and other applicable biometric privacy statutes.
Salary Context
This $126K-$194K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Zayo Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($160K) sits 24% below the category median. Disclosed range: $126K to $194K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Zayo Group AI Hiring
Zayo Group has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in US. Compensation range: $194K - $219K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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