AI Agent Engineer

About the role XtendOps builds AI agents that handle real work for enterprise clients — reading incoming requests, deciding what to do, calling tools across the client's systems, and either acting or preparing work for a human to approve. These agents run against live customer traffic every day. You will own one or more of them outright: design the flow, build the tools, wire the integrations, ship it, and keep improving it. This is a backend engineering role — we build systems around models, we don't train them. Key Responsibilities Build and ship AI agents end to end, from design through production Design and implement the tools and MCP servers agents call, including schemas and descriptions that models use correctly Integrate third-party APIs and internal services behind those tools — auth, retries, rate limits, idempotency Write and iterate the agent instructions that drive behaviour Configure the agent loop: model selection, turn limits, reasoning effort, tool permissions Debug agent behaviour in production — wrong tool, wrong arguments, no tool call, early stop - Deploy with Docker to cloud runtimes and instrument runs so they can be debugged after the fact Add new features and integrations to live agents without breaking what's already running Explain how an agent works to internal teams and, occasionally, to a client's engineers About you 3+ years building production backend software, with strong TypeScript and Node.js Hands-on experience building LLM agents that call tools — any framework (Claude Agent SDK, OpenAI, LangChain/LangGraph, Vercel AI SDK, or your own loop) Practical experience with MCP or equivalent tool-integration patterns Solid REST API integration experience against third-party systems Comfortable with Docker and at least one cloud platform (AWS, GCP or Azure) Git, testing and code review as normal working habits Makes decisions and takes initiative. You choose the model, the flow and the tool surface without being told, flag problems nobody has noticed yet, and propose fixes — including to infrastructure you don't own Clear written communication in English Nice to have Claude Agent SDK or the Anthropic API in production Writing MCP servers, not just consuming them Evaluating LLM output systematically — test sets, regression checks, eval harnesses AWS hands-on: ECS/Fargate, Lambda, IAM, DynamoDB Customer service platforms — Gladly, Zendesk, Salesforce, Amazon Connect Python for data work