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About This Role
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.
Zayo is seeking a highly strategic and technically accomplished Director, Software Engineering – Network Automation and AI Solutions to lead the transformation of how we design, engineer, operate, and support our network infrastructure through automation, artificial intelligence, advanced analytics, and modern software engineering practices.
This leader will be responsible for defining and executing the technology strategy across network automation, OSS platforms, operational tooling, AI-enabled engineering workflows, network correlation, inventory management, ticketing integration, and network triage systems. The Director will provide architectural leadership and technical direction for software engineering teams developing scalable solutions that improve operational efficiency, service quality, and customer experience across Zayo's network operations and engineering organizations. The ideal candidate combines deep telecommunications and network operations expertise with modern software engineering leadership, leveraging AI, automation, and orchestration technologies to accelerate delivery, reduce operational complexity, and enable a more proactive and intelligent network operating model.
Key Responsibilities
- Define and execute the strategic roadmap for network automation, OSS modernization, and AI-driven operational transformation.
- Lead and develop software engineering teams responsible for network automation, OSS platforms, and operational tooling.
- Establish architectural standards, engineering best practices, and scalable solutions across monitoring, inventory, correlation, ticketing, and network management systems.
- Drive adoption of AI, automation, and advanced analytics to improve network operations, service delivery, and engineering workflows.
- Identify, prioritize, and deliver high-impact automation initiatives that improve operational efficiency, service quality, scalability, and customer outcomes.
- Provide technical leadership for the design, development, integration, and support of automation platforms and enterprise OSS applications.
- Partner with business and technology leaders to align automation investments with strategic objectives and operational priorities.
- Establish engineering metrics, performance standards, and KPIs to measure delivery excellence and business impact.
- Lead operational transformation initiatives that reduce manual effort, improve reliability, and accelerate incident detection and resolution.
Collaborate across Engineering, Network Operations, Product, Security, Architecture, and IT to drive enterprise-wide automation and technology initiatives. *
Additional Responsibilities
- Evaluate emerging technologies, AI platforms, LLM frameworks, and automation capabilities to support innovation and continuous improvement.
- Serve as a trusted advisor to executive leadership on automation, software engineering, AI strategy, and operational modernization.
- Influence enterprise technology decisions through technical expertise, business acumen, and organizational leadership.
- Foster a high-performance engineering culture focused on innovation, accountability, continuous learning, and customer success.
Communicate technical strategies, roadmap progress, and business outcomes effectively to technical and executive audiences. *
Required Qualifications
- Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Telecommunications, or a related field; equivalent experience will be considered.
- Minimum of twelve (12) years of experience in software engineering, network automation, OSS development, network operations, or related technical disciplines.
- Minimum of five (5) years of engineering leadership experience managing software development or automation teams.
- Hands-on experience with Cisco Crosswork Network Controller (CNC) and Cisco Network Services Orchestrator (NSO), including network automation, orchestration, and service lifecycle management.
- Proven experience leading large-scale automation and operational transformation initiatives.
- Strong expertise in software architecture, distributed systems, and enterprise application development.
- Hands-on experience with Python, JavaScript, and modern development frameworks.
- Experience designing and implementing AI-enabled automation solutions in production environments.
- Strong knowledge of networking fundamentals including TCP/IP, DNS, SSL, HTTP(S), SSH, routing, switching, and network management principles.
- Experience with network monitoring and management technologies including SNMP, Syslog, telemetry, and event management systems.
Exceptional analytical, problem-solving, communication, and organizational leadership skills. *
Preferred Qualifications
- Telecommunications, fiber network, carrier, or large-scale infrastructure industry experience.
- Experience with IBM/Tivoli Netcool, ServiceNow, Salesforce, Splunk, or similar OSS and operational platforms.
- Knowledge of network inventory, service assurance, fault management, and operational support systems.
- Experience with LLMs, prompt engineering, AI orchestration frameworks, agent-based architectures, and enterprise AI adoption strategies.
- Familiarity with cloud-native architectures, APIs, microservices, and modern integration platforms.
- Experience building AI governance frameworks and responsible AI operating models.
Knowledge of network engineering, service delivery, and customer-facing operational processes. *
Success Measures
- Increased automation adoption across network engineering and operational workflows.
- Reduction in manual operational effort and process cycle times.
- Improved network reliability, service quality, and incident resolution performance.
- Successful delivery of strategic OSS modernization and AI transformation initiatives.
Measurable operational savings and productivity improvements. *
Estimated base salary range: $142,600 - $219,400 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 $142K-$219K range is below the median for AI Software Engineer roles in our dataset (median: $192K across 146 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 8,943 AI roles we're tracking, AI Software Engineer positions make up 4% of the market. At Zayo Group, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,448 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($181K) sits 18% below the category median. Disclosed range: $142K to $219K.
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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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