Operations Engineer

Description

What is the company

The company is re-imagining the trillion-dollar U.S. B2B card payments infrastructure, with a focus on the country’s 500,000 commercial fleets, 40 million commercial vehicles, and many millions of commercial drivers. The incumbent technologies that cater to these customers are decades old, and businesses with fleets increasingly demand modern digital experiences and transparent financial services products. The company’s mission is to deliver this at a transformational scale, beginning with the company Fleet and Fuel Card built on a cutting-edge spend management platform.

The company builds payments and spend management for fleet businesses: fuel cards, corporate cards and bill pay. We’re becoming AI-native: AI handles the repeatable work, so people can focus on customers and the hardest problems.

Our sales team already runs on AI workflows we built in-house. Now we're building a small internal AI team to take that across the whole company. You'll join as one of two AI Operations Engineers, reporting to our AI Lead.

About the Role

This is a builder role with an operator’s mindset. You’ll find the highest-leverage work across Sales, Customer Success, Marketing, Support, Finance, Risk, Product, and Operations, and own it end to end, from first conversation to production. Some weeks you’ll write code; others you’ll run a workshop or redesign a process with a team.

Everyone at the company uses Claude and builds their own skills. You build what they can’t: autonomous agents, and the foundation that makes everyone’s skills better.

You’ll drive measurable gains in the company’s operating efficiency, tracked in hours saved.

Responsibilities

• Find and prioritize the work. Sit with teams, map their workflows, and size the time saved. Turn what you find into a ranked backlog with the AI Lead.
• Build autonomous agents. Ship event-driven agents that do multi-step work across our stack. You’ll own the engineering off-the-shelf tools can’t do:
• Loop and orchestration: design agent loops, sub-agent handoffs, and when to act alone or escalate to a human.
• Memory and context: model shared memory as a knowledge graph, and decide what each agent sees, remembers and forgets.
• Custom tooling: build the tools our agents need.
• Traces and evals: trace every run, score outputs against test sets built from real outcomes, and prove each version beats the last.
• Measure impact. Define success metrics for each build, track adoption and time saved, and report results to leadership.
• Run what you ship. Own reliability, latency and cost in production, and catch regressions before users do. Partner with Security and IT on access, audit trails and the handling of sensitive data.
• Enable the company. An AI-native company runs on self-service, and self-service needs solid infrastructure. You’ll architect the shared foundation the rest of the company builds on: knowledge bases, application databases, templates, tools, and connectors. Then you’ll help people use it through training, office hours and hackathons, and grow a network of AI champions.
• Stay on the cutting edge. Try new models and tools as they land, measure them against what we run today, and keep raising the bar on everything you’ve built.
• Projects you might take on
• Autonomous product help docs. Build a brain that captures questions from customers (support tickets and calls) and employees (Slack). It spots missing or outdated articles, drafts updates, sends them for review, and learns from what gets approved and shared with customers.
• Deal coach for large trials. Build an agent that creates value calculators and tailored pitch decks from call transcripts and account data. It coaches AEs and CSMs through each trial with next steps, and learns which plays win.

Requirements

• 2–5 years in operations, product, solutions or forward-deployed engineering, internal tools, or a similar role at a fast-moving company.
• A track record of shipping AI workflows or automations that people use every day. Show us what you built.
• Hands-on fluency with Claude Code, Cursor or similar coding agents, plus LLM tooling such as prompt design, tool use and MCP.
• You've built at least one agent with persistent memory or a feedback loop that made it better over time.
• Experience with evals: building test sets, scoring outputs and using the results to improve an agent.
• Comfort writing and reading code: SQL, plus Python or TypeScript scripting, APIs and webhooks.
• Clear communication with non-technical teammates. You can teach as well as build.
• High agency: you spot inefficient processes and fix them without waiting to be asked.
• A software engineering background.
• Experience building with the MCP servers or APIs for tools like Salesforce, Notion, Slack and Gmail.
• Experience with guardrails or monitoring for AI systems.
• Background in fintech, payments or another regulated industry.
• Startup experience, especially at an AI-native company.

Conditions

Our salary ranges are based on paying competitively for our size and industry, and are one part of our total compensation package that also includes benefits and equity. Pay decisions are based on a number of factors, including scope and qualifications for the role, experience level, skillset, and balancing internal equity relative to other the company employees. We expect the majority of candidates who are offered roles at the company to fall healthily within the range based on these factors.

Equity Grant : Meaningful potential upside given the company’s early-stage trajectory

Benefits

• Medical, dental and vision insurance
• Flexible paid time off (vacation, personal well being, paid holidays)
• Tools to help manage your financial wellness, including webinars, access to an equity tax advisory service, and company-sponsored 401(k)
• Paid parental leave
• $400 accessories allowance (a keyboard, mouse, headphones, etc.)
• Education stipend
• Free lunch every Friday
• Check out the latest podcast interview on Fintech Layer Cake with the company Founder Daniel Simon !