Own the backend of Smeetz’s MCP server and AI chatbot, including APIs, integrations, agent logic, guardrails, reliability, observability, and evaluation systems. Spend approximately 70% of the role on backend engineering and 30% on product collaboration, usage analysis, prioritization, and success measurement. The role requires production ownership, on-call participation, documentation, and experience shipping LLM-powered features to real users.
Smeetz is an AI-powered unified commerce platform for the European leisure and entertainment industry. Theme parks, family entertainment centres, museums, zoos, and theatres run their ticketing, seating, F&B, and payments on us.
This role owns the backend of our two AI surfaces: the Smeetz MCP server and the AI chatbot. Both are backend services at heart, made of APIs, integrations, and agent logic sitting on top of our commerce platform. Expect to spend about 70% of your time on backend engineering (building, scaling, and running these services in production) and about 30% on the product side (understanding how venues use them, shaping what gets built next, and measuring whether it worked).
You report to the VP of Engineering and have no direct reports.
Backend
- Own the backend of the Smeetz MCP server and AI chatbot end to end, covering the services, the tool surface, the agent logic, and the guardrails on what they are allowed to do.
- Design and build the APIs and integrations that connect our AI surfaces to the Smeetz platform (ticketing, bookings, payments) with the security, performance, and reliability that production commerce requires.
- Own reliability and performance, covering latency, error rates, cost per call, and the call patterns clients actually generate. You get full observability data (Langfuse) and the mandate to act on it.
- Build the eval layer so prompt and model changes are measured before they ship, not after a customer notices.
- Participate in on-call for the services you own.
Product
- Work with the product owner and venue-facing teams to understand how operators use the AI surfaces, and turn that into technical specs and priorities.
- Read traces and usage data to spot what is breaking or missing, and propose what to build next.
- Define success metrics for what you ship and report back on them.
- Document the surfaces you own so the next engineer can work on them without you in the room, and extend our shared Claude Code skill library.
RequirementsProfessional requirements
- 5+ years shipping production code, including 3+ years of backend engineering building and running production APIs. Node.js/TypeScript is strongly preferred.
- Solid backend fundamentals, covering API design, data modelling, authentication and permissions, queues and async processing, and debugging under production load.
- You have shipped at least one LLM-based feature to real users and worked on its backend side, including tool calling, structured outputs, cost and latency control, and production failure modes. Prototypes and demos do not count.
- Ownership of a production system, including the on-call that comes with it.
- Daily use of Claude Code or an equivalent agentic environment beyond autocomplete.
- Comfortable talking to non-technical stakeholders and turning their problems into technical work.
- Fluent English.
Nice to have
- Designing a tool or API surface for agents, with naming, granularity, and error messages a model can recover from.
- The MCP specification, beyond using it as a client.
- The Anthropic Claude API and tool use.
- Langfuse or a similar tool (LangSmith, Braintrust, Phoenix).
- Evals and retrieval over a support knowledge base (Intercom Fin experience is relevant).
- Guardrails for agents with write access, covering permissions, confirmation steps, prompt injection, and PII.
- Experience in a product-engineering setup where you shaped priorities, not just tickets.
- French or German.
Personal attributes
- Owner: The services you run work because you are responsible for them.
- Backend-first, product-aware: Starts from solid engineering, and knows why the feature matters to the venue.
- AI-native: Uses Claude and agents as everyday leverage.
- Measured: Defines success before building and accepts the number.
- Writer: Leaves the codebase better documented than they found it.
Benefits
- A published dual career ladder. Grow to Staff AI Product Engineer without becoming a manager, on pay bands comparable to the management track.
- Full ownership of two AI surfaces that are core product, not a side project.
- Direct contact with the venue operators who use what you build.
- Modern AI-native tooling: Claude Code with a shared internal skill library.
- Fully remote position.
- Competitive salary based on experience. Band to confirm.
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