AI Ops Agent Engineer

Arbitr Group Ltd; Dublin; Hybrid; EUR 120,000 - 180,000; Full time

Job summary

Arbitr is building an autonomous enterprise powered by intelligent agents to automate day-to-day business operations across functions. As an AI Ops Agent Engineer, you will architect, plan, build and integrate agents into Arbitr's existing agent suite, handing over deployment details to IT and Infrastructure.

You will leverage Python (Pydantic AI) and n8n to build and integrate solutions, instrument everything with Langfuse, and design for failure to ensure graceful degradation.

Qualifications

• LLM fluency and practical command of model behavior, including prompt design, tool calls, and failure modes.
• Strong Python skills with maintainable code.
• Ability to quickly absorb a business process and identify automatable core.
• Bias toward shipping working solutions under real-time pressure.

Responsibilities

• Map processes with function owners and decompose tasks for system ownership.
• Build automations and AI agents using Python stack (Pydantic AI) and n8n.
• Integrate new agents into Arbitr's existing agent suite and ensure end-to-end product cohesion.
• Write evals to measure accuracy, cost, and latency.
• Instrument agents with Langfuse and monitor traces for reliability.
• Design for failure by defining autonomy vs human intervention and handling API outages gracefully.
• Keep prompts, payloads and system instructions model-agnostic to enable provider swaps.

Skills

LLM fluency

Python

Process comprehension

Shipping mindset

Tools

Langfuse

MCP

n8n

Pydantic AI

Job description

Arbitr is moving beyond simple automation. We are building an autonomous enterprise — an army of intelligent agents running day-to-day business operations instead of people doing them by hand.

As an AI Ops Agent Engineer, you sit in the engine room. You find the manual processes eating the most internal time across Production, Sales, Marketing, Finance, HR and Customer Success, and you replace them with agents that work reliably in production.

This is a building role. You will architect, plan, build and integrate agents into Arbitr's existing agent suite. You will not be responsible for deploying or running the infrastructure — that sits with our IT & Infrastructure team — but you will need to understand how deployment works well enough to hand over clean, actionable requirements.

What You'll Actually Do

• Map processes. Sit with a function owner, understand what they do all day, and decompose it into steps a system can own. This is the part most people get wrong.
• Build automations and AI agents. Our core stack is Python (mainly Pydantic AI), plus n8n for lighter-weight workflow automation.
• Integrate, don't isolate. New agents plug into our existing suite and consume MCP tools built by our tool developers. Everything must compound into a product, not stack up as one-off projects.
• Write the evals. Create measurements of accuracy, cost and latency.
• Instrument everything. All agents are traced in Langfuse. Uncertainty is fine; silent failures are not.
• Design for failure. Define where the agent runs autonomously, where a human is required, and what happens when an external API goes down. Operations should degrade gracefully, never stop.
• Keep the boundary clean. Prompts, payloads and system instructions stay model-agnostic so we can swap providers without a rewrite.

What We Need

Mandatory

• LLM fluency. Deep, practical command of how these models behave: prompt design, tool and function calling, structured outputs, context management, failure modes, and — critically — knowing when not to reach for a frontier model.
• Strong Python. You write code that other engineers can maintain.
• Process comprehension. You can absorb an unfamiliar business process quickly, spot the automatable core, and be honest about what shouldn't be automated. We weight this as heavily as the code.
• A bias toward shipping working things. We are under real pressure to remove manual hours. High-impact and unglamorous beats flashy every time.

Beneficial

• Building eval frameworks and measuring agent quality systematically
• Autonomous, self-healing systems — retries, fallbacks, recovery without human intervention
• Deployment processes and CI/CD pipelines
• Model Context Protocol (MCP)
• Fine-tuning small or open-weight models for bounded, high-volume tasks
• Observability and tracing tooling (Langfuse or similar)

Working Here

You’ll join a small, senior, multidisciplinary team split across Spain, Ireland and Auckland. The Auckland overlap is real: expect some early‑morning calls, including a weekly team meeting. In exchange you get a genuinely fast environment with very little bureaucracy between an idea and production.

Not The Right Fit If

• You want to build top-level orchestration before there are agents to orchestrate.
• You’d rather research approaches than ship one that works.

Initially this will be a fully remote position - with a view to move to 3 days in Dublin office - with an employment contract based in Ireland.