AI Enablement and Workflow Lead

AI Enablement And Workflow Lead

Greater NYC Area

Canopy is a healthcare safety technology company building the connected safety platform for healthcare teams. Our products help hospitals and health systems protect frontline staff, respond faster in high-intensity environments, and create safer places to work.

We're at an important stage of growth: scaling our products, our operating systems, and the way our teams work together. We believe AI can help us move faster, improve quality, and create more space for higher-impact thinking, but only if it's adopted thoughtfully, securely, and practically.

This role exists to make Canopy an AI-native company in how we build, operate, support customers, and make decisions. This is a hybrid role in the New York City metro area.

We're hiring an AI Adoption Accelerator: someone who lives at the frontier of agentic AI and can pull the rest of the company up to that frontier with them.

You do not need to be a career software engineer. You do need to be the person who's already built a dozen agents, knows how the current frontier models differ in practice, has strong opinions about context engineering, can stand up an MCP server in an afternoon, and won't ship an agent without an eval behind it. And you need to be able to sit next to someone who has never written a prompt and leave them able to build the next workflow themselves.

The job has two halves, and they matter equally:

• Build. Embed with teams across Canopy (CX, Sales, Marketing, Product, Engineering, Operations, Finance, People), map their work, and turn the highest-leverage workflows into real AI tooling. Sometimes that's a Claude Project with the right skills and connectors. Sometimes it's a multi-step agent wired into our systems. Sometimes it's a sharp prompt that ends a problem someone has been dragging through their day for a year.
• Teach. Every build is also a tutoring session. By the time you ship something with a teammate, they should understand enough to build the next version without you. The goal is not to become the bottleneck, it's to leave behind people who can pattern-match on their own, and to compound Canopy's capability with every workflow.

• In the first 90 days, you'll understand Canopy's priorities, systems, and team pain points; stand up the experimentation operating rhythm with the early-adopter group; build an initial cross-functional use-case inventory; ship several real AI workflows with named owners and eval-backed success criteria; and launch the first version of Canopy's internal AI hub.
• In the first 6 months, you'll have shipped and scaled meaningful workflows across multiple departments, created a measurable lift in how confidently people use AI, reduced manual work in priority workflows, and established a repeatable path from idea → experiment → demo → adoption → scale with the evals to prove the agents hold up.
• In the first year, you'll have built a durable internal capability for AI building, enablement, and adoption; demonstrated measurable business impact through faster execution, better quality, and less manual work; and helped create a culture where teams don't just "use AI" but continuously rethink how the work should be done.

• Frontier LLM fluency. Deep, current, hands-on experience with Claude, GPT, Gemini, and open models. You can explain how they differ in practice, not just on benchmarks.
• Agentic systems experience. You've built real things with tool use, planning, memory, and multi-step orchestration, and you've debugged them when they went sideways.
• MCP fluency. You've written MCP servers, integrated clients, and built connectors into the systems teams actually use.
• Context engineering instinct. Strong, defensible opinions about what goes in the window, what gets retrieved, summarized, or evicted, and when to break your own rules.
• Evals discipline. You don't ship on vibes. You build eval harnesses and can articulate why your agent works, or why it doesn't yet.
• Enough code to be dangerous. You write Type Script or Python to glue real systems together, even if you'd never call yourself a software engineer.
• A…