Director, Artificial Intelligence (AI)

Ziply Fiber

3d•$165,000 - $200,000•Remote

About The Position

Ziply Fiber is seeking a Director of Artificial Intelligence (AI) to lead the company’s AI vision, roadmap, and execution with a strong emphasis on hands-on delivery and measurable business outcomes. This leader will build and scale AI capabilities that improve customer experience, streamline operations, and enhance network performance across the enterprise. This role is ideal for a highly technical and pragmatic leader who can personally engage in solution design, experimentation, vendor evaluation, and implementation while also leading cross-functional teams. The Director will focus on reducing churn, lowering customer complaints, reducing avoidable dispatches, and delivering automation across Customer Care, Field Services, Dispatch Centers, IVR, Digital Channels, and Network Automation. Success in this role requires balancing innovation, speed, governance, and business value while establishing AI as a strategic differentiator for Ziply Fiber. The Director will help move the organization from isolated pilots to scalable, production-grade AI solutions that deliver tangible impact for customers, employees, and network operations.

Requirements

Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
Minimum of ten (10) years of experience in AI architecture, data science, or software engineering, including large-scale production deployments of machine learning, deep learning, or AI-driven systems in enterprise environments, with five (5)+ years in leadership roles.
Demonstrated ability to design cloud-native and edge AI architectures, integrating models, APIs, and agents into enterprise platforms.
Proficiency with multi-agent orchestration using Model Context Protocol (MCP), Agent-to-Agent (A2A) interaction models, retrieval-augmented generation (RAG), vector databases, and context memory architectures.
Proven experience designing and implementing enterprise-grade AI platforms leveraging both classic machine learning techniques such as forecasting, optimization, and predictive modeling, and modern generative and agentic frameworks.
Proficiency with cloud-scale AI ecosystems such as Microsoft Copilot, Azure AI, OpenAI, AWS, or Google Cloud, and strong familiarity with modern data governance platforms.
Proficiency in Python and familiarity with SQL, R, or Java.
Hands-on experience with frameworks such as TensorFlow, scikit-learn, and Hugging Face, as well as workflow orchestration or MLOps tools such as MLflow, Kubeflow, and Airflow.
Experience implementing governance, monitoring, and Responsible AI practices that ensure safety, transparency, reliability, and production readiness.

Nice To Haves

Master’s degree in related field.
Experience in telecommunications, network operations, customer care, or digital service organizations.
Familiarity with use cases such as churn prediction, complaint reduction, dispatch optimization, IVR automation, conversational AI, digital self-service, and network event intelligence.
Experience with cloud-based AI platforms such as Azure AI, AWS, or Google Cloud.
Strong executive communication, stakeholder management, and vendor evaluation skills.

Responsibilities

Define and lead Ziply Fiber’s enterprise AI strategy and roadmap, aligned to business priorities and digital transformation goals.
Establish a centralized AI operating model that reduces fragmentation and accelerates enterprise-wide adoption.
Partner with executive leadership to identify, prioritize, and sequence high-impact AI use cases with measurable business outcomes.
Act as a hands-on leader who can move from strategy to prototype to production with urgency and discipline.
Lead the design, development, and deployment of scalable AI and machine learning solutions across customer care, field services, dispatch centers, IVR, digital channels, and network operations.
Develop AI-driven capabilities that reduce churn, lower complaint volumes, improve first-contact resolution, and reduce avoidable truck rolls and dispatches.
Drive automation use cases such as agent assist, intelligent routing, conversational AI, digital self-service, predictive dispatching, workforce recommendations, and network event correlation.
Own end-to-end execution from ideation through production deployment, performance monitoring, and value realization.
Collaborate with data, IT, engineering, and operations teams to build robust AI and ML platforms, data pipelines, and reusable services.
Ensure the availability, quality, and governance of data required to support AI use cases across customer, operational, and network domains.
Champion modern AI tooling, MLOps practices, observability, and engineering standards that enable scalable and reliable deployment.
Remain hands-on with architecture, experimentation, model evaluation, vendor selection, and technical trade-off decisions.
Establish and enforce AI governance frameworks that ensure compliance, security, privacy, and ethical use of AI solutions.
Define standards for model lifecycle management, explainability, monitoring, and accountability.
Mitigate risks related to bias, hallucinations, data quality, cybersecurity, and regulatory requirements.
Ensure business processes, controls, and adoption plans are designed for sustainable production use rather than isolated pilots.
Work closely with customer care, field services, dispatch, network operations, product, finance, and IT teams to identify friction points and deploy AI solutions that improve outcomes.
Serve as a thought leader who raises AI literacy, builds trust, and accelerates adoption across the organization.
Translate business needs into technical execution plans and ensure initiatives remain aligned to measurable value and business priorities.
Build and lead a high-performing team of data scientists, ML engineers, AI engineers, and analysts.
Develop internal AI capabilities while strategically leveraging external partners, vendors, and platform providers.
Create a culture of experimentation, accountability, rapid learning, and continuous improvement.
Coach teams to move quickly while maintaining strong engineering discipline, governance, and operational supportability.
Own and evolve technical standards, operational procedures, diagrams, and design documentation.
Ensure high quality Ops guides, MOPs, and runbooks are produced and maintained.
Conduct technical research and stay current on emerging technologies and industry best practices.
Performs other duties as required to support the business and evolving organization.