AI Enablement Engineer

What's the Short Version?

The AI Enablement Engineer designs, builds, and deploys AI-powered workflows, agents, and integrations that solve real business problems across San Mar and helps teams adopt and sustain them. This role works with solutions through their full lifecycle, from prototype to production to ongoing maintenance, while also coaching employees and translating between business needs and technical capabilities.

Additionally, this role produces the tools, automations and integrations required for users to enable AI capabilities. It pairs a practitioner's technical depth - coding, APIs, and agent orchestration with communication skills to evangelize new capabilities, execute demos, and make AI approachable for non-technical users. The Engineer partners closely with IT, Architecture, and business teams to ensure what gets built is secure, governed, measurable, and genuinely useful.

• Design, build, and deploy production-grade AI workflows, agents, and automations using approved platforms (Microsoft Copilot Studio, Power Automate, Azure AI, and equivalent tools).
• Integrate AI tools with enterprise systems via APIs, connectors, and protocols such as MCP (Model Context Protocol) — including authentication, error handling, and data flow.
• Build and orchestrate multi-step and multi-agent workflows, including the guardrails, monitoring, logging, and fallback logic that make them reliable in production.
• Develop reusable components, prompt libraries, skills/plugins, and integration patterns that other teams can adopt and extend.
• Work with solutions across their full lifecycle: prototype, test, deploy, monitor, maintain, and retire — taking proof-of-concepts through to supported production where the value justifies it.
• Design and operate within governed AI experimentation environments, building safely within approved guardrails.
• Use diagnostic questioning to uncover the underlying business need behind a request, separating what users ask for from what would actually help.
• Partner with employees and teams to understand current workflows, friction points, and productivity challenges, and identify where AI can meaningfully improve work.
• Provide 1:1 and small-group coaching, run workshops, office hours, and live demos, and deliver role-based AI literacy content in partnership with Talent Development.
• Translate between business language and technical capability — helping users articulate needs and helping technologists understand business context.
• Develop practical job aids, documentation, and reference materials that make AI approachable for non-technical users.
• Track adoption, usage, and outcome trends to identify gaps, measure impact, and prioritize what to build next.
• Perform other duties as assigned.
• Comply with all policies and standards.

• Bachelor's Degree in Computer Science, Information Technology, Engineering, Business, a related field, or equivalent practical experience.
• 1-3 years at minimum in technical or building roles (software development, automation, solutions engineering, creative operations, or similar), including recent hands-on experience building AI-powered or automated solutions.
• High level of technical acumen for learning and applying technology.
• Prior experience coaching, mentoring, or evangelizing technology to peers and leadership.
• Demonstrated coding ability (e.g. Python, JavaScript/Type Script, SQL) sufficient to build, integrate, and debug workflows, scripts, and automations independently.
• Hands-on experience building automations and integrations on platforms such as Power Automate, Copilot Studio, n8n, Zapier, or equivalent.
• Practical experience working with APIs, webhooks, and authentication, and connecting AI tools to enterprise systems and data.
• Experience building AI agents and orchestrating multi-step or multi-agent workflows, including error handling and reliability considerations.
• Working knowledge of core AI concepts — large language models, prompt engineering, AI agents, and how to evaluate, ground, and constrain AI outputs for reliability.
• Solid understanding of responsible AI principles, security, data privacy, and why guardrails…