Applied AI Engineer

About Dwelly Dwelly is building the AI operating system for residential lettings. Its growing network of agencies provides its AI with real-world data, continuous feedback, and control over complete workflows, making exceptional service the standard for landlords and tenants. Today, Dwelly operates more than 15,000 properties and $470 million in GMV, making it one of the UK’s ten largest lettings operators. The company has raised $263 million.

We’re a fast-growing, product-focused company, backed by top-tier investors and led by a team with deep experience in real estate, technology, and operations.

Position Summary We’re looking for an Applied AI Engineer to help build the agentic infrastructure that powers automation across Dwelly. Our growth model is acquisition. Every agency we acquire brings new operational workflows, legacy processes, and manual work. If we manage each new agency manually, the economics of the model break. Our ability to scale depends on turning complex operational processes into reliable AI-driven and automated systems.

We already have successful AI-driven flows running in Tenant Find and Property Management. They work and deliver real business results. But they were built quickly and as separate systems.

The next challenge is to bring these approaches together: build reusable agentic infrastructure, establish a robust evaluation and observability layer, and create systems that allow us to automate new workflows quickly and reliably as Dwelly scales.

This is not an AI research role. You will be building AI systems that operate inside a real business and are expected to work reliably in production.

We’re hiring remotely across the UK, Ireland, and European time zones. Candidates should be based within these regions/time zones to enable effective collaboration with the wider team.

Key Responsibilities

1. Agentic infrastructure

• Design and build the core primitives behind our agentic systems, including memory, context management, tool-calling, orchestration, and feedback loops.

• Move us from one-off AI solutions toward reusable infrastructure where new workflows can be introduced quickly and with predictable reliability.

1. Evaluation & observability

• Build the evaluation framework that allows us to understand how our agents perform and why they succeed or fail.

• Make testing, tracing, debugging, and evaluating AI systems as fundamental to our engineering process as unit testing traditional software.

• Develop the systems that allow us to confidently assess an agentic workflow before deploying it into production.

1. Agentic development

• Push forward how we use LLMs to build software itself.

• Create workflows where coding agents and specialized subagents can explore repositories, implement changes, review architecture, check conventions, run evaluations, and iterate with minimal human coordination.

• Use agentic engineering workflows extensively in your own day-to-day development.

1. Orchestration

• Design systems where specialized agents, tools, deterministic software, and humans work together effectively.

• Understand when a problem should be solved with an LLM, when traditional software is the better solution, and when multiple coordinated agents can materially improve the outcome.

1. Operational automation

• Act as the bridge between complex operational workflows and engineering.

• Work closely with our operational and product teams to understand how work actually happens across acquired agencies, identify the highest-leverage opportunities for automation, and turn them into reliable production systems.

1. Architectural influence

• Help define the architectural patterns we use for agentic systems as the company scales.

• You won’t be joining as engineer #20 on an established AI platform. You will be an early core member of the team with significant influence over our technical approach, tooling, and engineering standards.

Qualifications and Preferred Background

Stro...