Sr. Full-Stack Engineer, AI-Native; LATAM​/Europe - Remote

• Rate: $85 – $90/hr
• Hours: 40 hours / week
• Experience: 5 - 10 years
• Location: United States | Canada | Europe + 6 more

You will build and ship features across the full stack of Reunion Marketing's products, from the database and APIs to the interfaces our teams and automotive dealer clients use every day. We are looking for a senior full-stack engineer who uses AI coding assistants as a core part of how they build, ships production code at a pace that would not be possible without them, and still owns the quality, security, and design of everything that goes out.

Reunion Marketing builds the platforms and AI products that our strategists and automotive dealer clients rely on, including the Altitude Intelligence platform. Our engineers build with AI coding assistants such as Claude Code as a standard part of the workflow, from prototype to production. This role works across three areas:

1. Client-facing applications

• Dashboards, reports, and tools that dealer clients and our teams use daily
• Every screen and every number has to be right in front of a client

2. Platform and data services

• APIs, integrations, and data pipelines behind our marketing product lines
• Every product and report depends on this data being complete and correct

3. AI-powered features

• Chat, report generation, and agentic workflows built on large language models
• Turns client data and our methodology into answers and next steps

You will own features end to end, and the defining questions are yours:

• How should this be built across the stack, and what is the simplest design that will hold up?
• Which parts of the work should an AI coding assistant do, and what must a person write or check?
• How do we know AI-generated code is correct, secure, and maintainable before it ships?
• What does "ready for clients" mean — and how do we prove it?

It is a senior, hands-on role with unusual visibility: what you ship either protects a client relationship, wins one, or grows one, and we expect engineering decisions to be made with that in mind.

• Production data at scale. Live performance data across five marketing product lines for hundreds of automotive dealerships, not a demonstration dataset.
• Daily users who depend on the output. Strategists, client success, sales, and external clients use what you build as part of their working day.
• Meaningful stakes. A bug does not stay inside a demo; it can reach a client conversation. The engineering standard follows from that.
• Genuine ownership. The product direction is set and shipping, and many architectural decisions, including how the team builds with AI coding assistants, are still open for this hire to make and defend.

This role calls for more than strong implementation. The engineer we hire will:

• Understand where each piece of work fits in the product and what it changes for our clients and teams — retention, expansion, hours returned — and let that understanding shape the engineering decisions.
• See the system end to end: how a schema choice shows up in a report, how an API change lands in a client conversation, how today's shortcut becomes next quarter's incident.
• Treat AI-generated code as a draft from a fast collaborator: useful, and always reviewed, tested, and understood before it ships.
• Explain technical trade-offs to strategists and executives in their terms, and be willing to defend a delayed release when shipping would put client trust at risk.
• Judge their own success by what ships, how reliably it runs, and the time it returns to our teams and clients.