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.

What you'll do

• 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.

What we're looking for

• 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.

Experience & level

• Mid-level (3–5 yrs): owns features, tools, and integrations inside an agent someone else shaped.
• Senior (6+ yrs): owns the agent and its architecture, and has kept a system running in production before.