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. 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 Chat, report generation, and agentic workflows built on large language models Turns client data and our methodology into answers and next steps 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. it can reach a client conversation. 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. Treat AI-generated code as a draft from a fast collaborator: useful, and always reviewed, tested, and understood before it ships. Judge their own success by what ships, how reliably it runs, and the time it returns to our teams and clients.



Full-stack feature delivery — features from the database schema and APIs through to the front end, built with AI coding assistants and held to production standards for testing, security, and multi-tenant data access. AI-assisted engineering practice — how the team uses Claude Code and similar tools day to day: project context and instruction files, custom commands and agents, review conventions, and the guardrails that keep generated code consistent with our architecture. AI-powered product features — chat, report generation, and agentic workflows built on large language models, including retrieval, tool use, structured outputs, and the evaluations that show they are accurate. Quality and testing — automated tests, CI gating, and code review that catch defects whether a person or an assistant wrote the code. Production reliability — monitoring, incident response, performance, and the rollbacks that follow. Architectural judgment — build versus buy, iteration speed versus client-safe controls, and where AI tooling helps versus where it adds risk. 6+ years of full-stack software engineering experience, including 1+ year building production software with AI coding assistants such as Claude Code, Cursor, or GitHub Copilot as a primary part of your workflow. Strength across a modern web stack (for example TypeScript, React, Node.js or Python, and SQL), with a record of well-structured production systems rather than glue scripts. ~ Deep,



hands‐on experience with AI coding assistants: structuring context for large codebases, writing project instructions and custom commands, running agentic multi‐step tasks, reviewing and correcting generated code, and knowing when to stop and write it yourself. ~ A feature you shipped substantially with an AI coding assistant, and the ability to walk through how you directed the work, what the assistant got wrong, and how you caught it. ~ Production debugging instincts — specific failures you have fixed across the front end, back end, and data layer, and a habit of instrumenting before guessing. ~ A record of owning the quality bar on a multi‐team product, including incident response, rolling back a bad release, and defending a delayed release to stakeholders who wanted to ship. ~ Domain experience in marketing analytics, SEO/SEM, paid media, local search, or answer‐engine visibility. Tests and evaluations are first‐class. No feature ships without them, and no regression is acceptable because it is old. The human is in the loop by design. AI assistants write a large share of our code, and a person still owns every change: they direct the work, review it, and answer it in production. Success in this role shows up in what you ship and how reliably it runs, in adoption by the teams and clients it serves, in the time it returns to our strategists, in how well the team builds with AI tools because of your work, and in the absence of defects reaching a client. how you used AI coding assistants to build it: what you delegated, how you directed and reviewed the work, and what you would change. an architectural decision across the stack, and the business reasoning behind it.

📌 (Remote) Full Stack Developer (Ceuta)
🏢 Braintrust
📍 Ceuta