Senior AI/ML Engineer

Job Description:

At Sparklight/Cableone and our family of brands, we keep our customers and associates connected to what matters most. For our associates, that means: a thriving and rewarding career, respect for the communities where they live and work, a focus on health and wellness, an excellent work/life balance, and an open and inclusive workplace.

We are open to hiring remote if we find the right talent in any of the following states: AL, AR, AZ,  FL, GA, IA, ID, IL, IN, KS, LA, MD, MO, MS, NC, ND, NE, NM, NV, OR, OK, PA, SC, SD, TN, TX, UT.

The Senior AI/ML Engineer will serve as the technical authority for AI/ML platforms, agent architecture, Model Context Protocol (MCP) strategy, context engineering, orchestration, governance, and AI-assisted experiences within Network Intelligence. This role will define how agentic systems safely consume network data, engineering knowledge, automation capabilities, and operational intelligence. The Senior AI/ML Engineer will establish reusable architectural patterns, development standards, evaluation practices, human approval controls, and governance requirements while providing technical mentorship to AI Engineers assigned to technology-domain delivery teams. The position will partner closely with the Senior Network Automation Engineer to maintain a clear boundary between deterministic network capability development and intelligent consumption of those capabilities.

What you will do to contribute to the company’s success

• Define and own architecture and technical standards for AI/ML platforms, agent frameworks, agent harnesses, and agentic workflows.

• Define MCP strategy, server integration patterns, tool contracts, access controls, and lifecycle standards.

• Design reusable patterns for agent orchestration, multi-agent coordination, long-running workflows, and escalation paths.

• Establish standards for context engineering, memory systems, retrieval, grounding, source attribution, and knowledge packaging.

• Define human-in-the-loop approval requirements, reasoning boundaries, tool execution safeguards, auditability, and governance controls.

• Create evaluation frameworks and acceptance criteria for correctness, safety, reliability, hallucination reduction, and tool execution.

• Define how agents consume network APIs, automation services, data products, procedures, and engineering knowledge.

• Partner with the Senior Network Automation Engineer to maintain the capability contract between Network Automation Engineering and AI Engineering.

• Review complex, high-risk, or net-new AI and agentic solution designs.

• Guide AI Engineers assigned to technology-domain delivery teams and establish reusable implementation patterns.

• Provide technical mentorship, design guidance, code review, and architectural support for AI-focused engineering resources.

• Partner with Platform Engineering on AI service hosting, deployment, monitoring, alerting, scalability, and production readiness.

• Partner with Data Engineering, NMS Engineering, Reporting Engineering, and Capacity Engineering to ensure agents use trusted and appropriately structured data.

• Coordinate conversational and AI-assisted user experience requirements with UI/UX and front-end contributors.

• Produce High Level Designs (HLDs), architecture decision records, technical standards, and implementation guidance.

• Apply secure software development, CI/CD, source control, testing, and operational support practices to AI solutions.

• Evaluate emerging AI/ML, agentic, orchestration, and MCP technologies for practical enterprise adoption.

• Communicate architectural decisions, technical risks, dependencies, and recommendations to engineering and leadership stakeholders.

Education and/or Experience

• Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Data Science, Information Technology, or a related technical field is preferred.

• Alternatively, 8 or more years of progressive experience in software engineering, platform engineering, data engineering, AI/ML engineering, or related technical disciplines will be considered.

• Five or more years of experience designing or delivering AI/ML, large language model, or agentic systems is preferred.

• Demonstrated experience leading technical architecture, establishing engineering standards, and guiding complex or net-new solution delivery.

• Strong Python development skills and experience building production-grade services and integrations.

• Experience with large language models, agent frameworks, tool calling, retrieval-augmented generation, context engineering, and model evaluation.

• Experience designing MCP servers, MCP clients, or comparable tool-integration architectures is strongly preferred.

• Experience with REST APIs, event-driven integrations, structured data, and enterprise system integration.

• Experience with vector databases, graph databases, knowledge graphs, semantic retrieval, or metadata-driven knowledge systems.

• Experience with cloud-based AI services, containerized deployment, Git, CI/CD, testing, monitoring, and production support.

• Experience applying security, governance, human approval, auditability, and responsible AI practices to production systems.

• Experience in telecommunications, ISP, network engineering, infrastructure, or operational technology environments is preferred.

Certificates, Licenses, Registrations

Specific Certifications are not required. There are a few that can demonstrate significant understanding of key concepts.

• Microsoft Certified: Azure AI Engineer Associate

• Microsoft Certified: Azure Solutions Architect Expert

• Microsoft Certified: DevOps Engineer Expert

• AWS Certified Machine Learning Engineer - Associate or AWS Certified Machine Learning - Specialty

• Google Cloud Professional Machine Learning Engineer

• Databricks Certified Machine Learning Professional

• Certified Kubernetes Application Developer or Certified Kubernetes Administrator

• Relevant responsible AI, cloud security, data engineering, or architecture certifications

Other Qualifications

• Demonstrated ability to distinguish deterministic automation responsibilities from agentic orchestration and AI-consumption responsibilities.

• Experience establishing reusable agent architectures, development standards, governance patterns, and evaluation methods.

• Experience with MCP, Semantic Kernel, LangGraph, LangChain, Azure AI services, Azure OpenAI, or comparable agent and orchestration frameworks.

• Experience with retrieval-augmented generation, embeddings, vector search, graph-based retrieval, and knowledge packaging.

• Understanding of AI safety, hallucination reduction, prompt injection risks, tool-use controls, auditability, and human approval patterns.

• Ability to evaluate AI-generated outputs and agent actions for correctness, safety, reliability, and operational impact.

• Ability to translate engineering procedures, operational knowledge, and business processes into governed agentic workflows.

• Ability to communicate complex architecture, risks, tradeoffs, and technical recommendations to technical and non-technical stakeholders.

• Strong technical leadership, mentoring, problem-solving, and cross-functional collaboration skills.

• Ability to work effectively with Network Automation Engineers, Platform Engineers, Data Engineers, NMS Engineers, Reporting Engineers, Capacity Engineers, Network Security, and Infrastructure Engineering.

• Adaptability and willingness to evaluate emerging AI technologies while maintaining disciplined production standards.

• Ownership mindset and accountability for architecture quality, production readiness, governance, and delivery outco