Applied AI Engineer (€90–130k p/a)

Fully Remote: UK, Ireland and European time zones

A hands-on engineering role building AI agents, reusable infrastructure and automation that support the everyday operations of a growing property technology business.

What’s In It For You:

Salary: €90,000–€130,000 p/a

Flexibility: Fully remote, hiring across the UK, Ireland and European time zones

Impact: Build systems that reduce repetitive operational work and improve the experience of people managing, letting and renting properties

Ownership: Join as an early core member of the engineering team, with significant input into architecture, tooling and engineering standards

Environment: A product-focused business with AI workflows already running in production and a clear need to expand their capabilities

Technology: TypeScript, Node.js, PostgreSQL, LLM orchestration and agentic workflows

Why This Role

nineDots is partnering with a growing property technology company building AI into the way residential lettings and property management operate.

The business grows through acquisitions. Each agency brings its own systems, processes and manual tasks, creating an engineering challenge: how do you support that growth without adding more administrative work every time?

They already have AI workflows delivering results across tenant sourcing and property management. Those systems were built quickly to solve individual problems. The next step is to bring them together into a reusable foundation that can support new workflows reliably.

They’re looking for an Applied AI Engineer to help build that foundation, taking ownership of the infrastructure, evaluation and orchestration behind it.

What They Need Help With:

You’ll work closely with product and operational teams to understand how work happens, identify useful opportunities for automation and turn them into dependable software.

The remit covers both the AI systems used by the business and how the engineering team uses agents to develop software.

You’ll be:

• Building reusable components for agent memory, context management, tool calling and feedback loops
• Designing workflows that coordinate AI agents, conventional software and human input
• Developing evaluation frameworks to assess performance before deployment
• Making agent behaviour easier to trace, debug and improve
• Investigating failures and handling operational edge cases
• Turning successful standalone workflows into infrastructure that other processes can use
• Building development workflows where coding agents can explore repositories, implement changes, review code and run checks
• Helping establish architectural patterns and engineering standards as the platform grows

A key part of the job is knowing where an LLM adds value, where conventional code is more dependable and where a person should remain involved.

What They’re Looking For:

• Strong software engineering experience, including independently taking complex systems from idea to production
• Hands-on experience shipping AI or agentic systems used in a real business
• Practical understanding of tool use, structured outputs, context management and orchestration
• Experience addressing reliability, latency, evaluation and observability
• Regular use of coding agents, with a considered approach to checking their work
• Strong architectural judgement and the ability to connect technical decisions to business needs
• Comfort taking ownership when requirements are still developing
• Clear communication and fluency in English

The environment is TypeScript-first, using Node.js and PostgreSQL. Engineers with strong Python-based AI experience are also welcome, provided they’re comfortable applying that experience in TypeScript.

The Environment:

You’ll be close to the people using the systems you build, with direct exposure to operational problems and feedback.

Success means making new workflows quicker to introduce, understanding how agents perform before they reach production and reducing the manual work needed to support growth.

This will suit someone who enjoys shaping the approach, asking questions and staying involved through deployment, debugging and ongoing improvement.

Interested?:

If you’ve shipped AI systems into production and want a role with broad engineering ownership, drop me a message or apply and we’ll chat.