Remote Artificial Intelligence Jobs in Bothell, WA

This is a remote position.

Position Title: Director, Artificial Intelligence (AI)

Base Salary: $165,000 to $200,000 annually DOE

Bonus: Target annual bonus

Benefits: Medical, dental, vision, 401k, flexible spending account, paid sick leave and paid time off, parental leave, quarterly performance bonus, training, career growth and education reimbursement programs.

Ziply Fiber is a local internet service provider dedicated to elevating the connected lives of the communities we serve. We offer the fastest home internet in the nation, a refreshingly great customer experience, and affordable plans that put customers in charge.

As our state-of-the-art fiber network expands, so does our need for team members who can help us grow and realize our goals.

Our Company Values:

• Genuinely Caring: We treat customers and colleagues like neighbors, with empathy and full attention.
  

• Empowering You: We help customers choose what is best for them, and we support employees in implementing new ideas and solutions.
  

• Innovation and Improvement: We constantly seek ways to improve how we serve customers and each other.
  

• Earning Your Trust: We build trust through clear, honest, human communication.
  

Job Summary

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.

Key Success Metrics

Success in this role will be measured by the ability to deliver measurable business outcomes and scale AI capabilities across the enterprise, including:

· Deployment and adoption of high-impact AI use cases across customer care, field services, dispatch, digital channels, IVR, and network operations.

· Measurable reductions in churn, customer complaints, repeat contacts, and unnecessary dispatches.

· Improvements in customer experience, operational efficiency, first-contact resolution, and network performance.

· Establishment of a scalable, governed AI ecosystem that enables sustained innovation and business value.

Essential Duties and Responsibilities:

The Essential Duties and Responsibilities listed below are a range of duties performed by the employee and not intended to reflect all duties performed.

AI Strategy & Leadership

· 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.

Solution Development & Execution

· 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.

Data & Platform Enablement

· 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.

Governance & Responsible AI

· 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.

Cross-Functional Collaboration

· 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.

Team Leadership & Capability Building

· 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.

Documentation, Standards & Best Practices

· 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.

Other Duties

· Performs other duties as required to support the business and evolving organization.

Required Qualifications:

· 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.

Preferred Qualifications:

· 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.

Knowledge, Skills, and Abilities:

· Ability to work independently and apply sound judgment and reasoning skills to a variety of situations.

· Ability to multi-task and collaborate effectively with other personnel to meet deadlines.

· Strong verbal and written communication, attention to detail, and organizational skills.