AI Operations Lead

About this role

Who We Are

Arcadia is the AI-powered energy intelligence platform for businesses. We replace fragmented tools and manual workflows with one platform to pay utility bills, buy energy, and advance sustainability — across every location, at enterprise scale.

Trusted by Fortune 2000 companies, Arcadia combines unified data, AI-powered analytics, and expert advisory to help enterprise teams save money, mitigate risk, and cut carbon .

We deliver this through three comprehensive solutions:

• Utility Bill Management: Automating the entire utility bill lifecycle — from data capture and validation to payment processing and auditing.

• Energy Procurement Advisory : Bringing together comprehensive data, AI-powered analytics, market expertise, and a strong partner network to make sophisticated procurement options accessible to all. .

• Sustainability Reporting — Verified emissions data with seamless integration into leading sustainability platforms.

Tackling the world's most complex energy challenges requires diverse thinking. We're building teams of people from different backgrounds, industries, and disciplines — united by a belief that energy management should be simple, intelligent, and a genuine driver of business value.

What we’re looking for:

Most companies adopt AI by bolting it onto how they already work. The teams that pull ahead go further: they rethink the workflow itself, so people spend less time on repetitive work and more on the problems that actually need them. AI is already gaining momentum across Arcadia. This role exists to make it take hold: to turn early wins into scaled, lasting change.

The AI Operations Lead is the operating layer of Arcadia's AI transformation. You will work directly with teams across the company, in product, engineering, go-to-market, finance, and operations, to learn how the work actually happens and bring AI into it. In practice that means designing and shipping the Claude skills, agents, and workflows teams adopt, and establishing the patterns, standards, and champion network that let those wins compound long after you have moved to the next team. The role reports into R&D Operations, the function that already connects these teams, and carries a mandate that spans all of them.

Outcomes are the bar. An AI workflow earns its place by what it changes for the team, not by how novel it is, and that standard decides what scales and what gets retired. You will hold the work to that test: adoption that holds, time given back to people for higher-value work, and workflows that outlast the team where they started. You are as fluent in designing a Claude skill as you are mapping a messy process or bringing a skeptical team along, and you solve a problem once, turn it into a pattern, and hand it to the rest of the company.

Arcadia is open to fully remote candidates and employees have access to co-working spaces.

#LI-REMOTE

What you'll do:

• Embed team to team. Rotate through teams across the company to map current-state workflows end to end: where time goes, where decisions stall, where handoffs between teams and functions compress or disappear. Translate that into a prioritized set of high-leverage AI opportunities for each team.

• Define and build Claude skills and workflows. Design, build, and ship reusable Claude skills, agents, and automations that teams actually adopt, not demos. Redesign the workflow around the capability and outcome rather than bolting AI onto an unchanged process.

• Engage and grow the champion network. Every team is expected to have an AI champion. In many cases you'll help identify who that should be, then enable them to extend, maintain, and evangelize workflows after you've moved on, so capability compounds instead of depending on you.

• Codify what works. Turn every win into a reusable pattern (a shared library of skills, templates, and playbooks) so a solution built for one team becomes a starting point for the next.

• Keep builds safe and compliant. Partner with the governance lanes (Security & Compliance; AI Technical Approach) so every workflow meets Arcadia's data-handling, classification, and human-in-the-loop standards by design.

• Measure and report impact. Track adoption, time saved, and workflow outcomes. Make the case for scaling what works and retiring what doesn't, and feed signal back to leadership.

What Success Looks Like (First Two Quarters):

• A repeatable engagement model for embedding with a team, mapping its workflow, and shipping its first production AI workflow.

• A growing, self-sustaining champion network across the company, with champions independently maintaining and extending what's been built.

• A shared skill-and-workflow library that measurably shortens the time to stand up the next team.

• Documented adoption and time-saved outcomes that move teams out of the pilot loop and into scaled use.

Must-haves:

• You bring 5–8 years spotting where workflows break down across departments, then working hand-in-hand with engineering to ship solutions: automations, AI workflows, internal tools that teams actually adopted, not just launched. Extra credit if you've done the building yourself.

• Strong workflow- and process-design instincts. You can sit with a team in any function, see the real operating model underneath the org chart, and redesign it.

• The interpersonal range to win trust across very different teams and cultures, and to bring a skeptic along.

• A bias for reusable systems over heroics. You'd rather build the pattern once than solve the same problem ten times.

• Comfort operating in ambiguity inside a fast-moving, post-acquisition organization.

Nice-to-haves:

• Experience standing up enablement, center-of-excellence, or champion-network models.

• Familiarity with AI governance concepts (data classification, human-in-the-loop, security review).

• Exposure to AI-native operating models at high-performing software organizations.

• Background in R&D, product, or engineering

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