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…