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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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

Strong software engineering background with experience independently delivering complex systems from idea through to production

Hands-on experience building AI or agentic systems that have operated in production

Experience dealing with real-world AI challenges such as reliability, latency, context management, failure modes, evaluation, and observability

Strong understanding of agents, tool use, structured outputs, context management, orchestration, evaluation, and feedback loops

Advanced practical use of modern LLMs — not simply using ChatGPT or Claude occasionally, but understanding how to build reliable workflows around probabilistic systems

Experience using coding agents and agentic development workflows as part of your own engineering process

Experience decomposing complex engineering tasks across agents or specialized subagents and designing mechanisms for them to verify and improve their outputs

Strong architectural judgment and ability to decide when to use LLMs, deterministic software, human intervention, or a combination of approaches

Experience with TypeScript / Node.js , or strong experience building AI systems in Python with the ability and willingness to apply that knowledge in a TypeScript-first environment

High autonomy, ownership, and comfort making decisions under ambiguity

Strong product judgment and ability to connect technical decisions to operational and business outcomes

Strong communication skills and fluency in English

Startup mentality: resilience, adaptability, and ability to thrive in a fast-paced environment

What We Don't Mean by “AI Experience”

For this role, AI experience does not mean:

Using ChatGPT to generate snippets of code

Adding a single LLM API call to an application

Building a prompt wrapper and calling it an agent

Blindly accepting whatever a coding agent generates

We are looking for engineers who think in terms of agents, tools, context, orchestration, evaluation, feedback loops, and human-machine collaboration — and who already apply these concepts in their day-to-day engineering work.

What Success Looks Like

Within your first 12 months:

Reliability: We can deploy a new agentic workflow and understand how it will perform before it reaches production because we have robust evaluation and observability systems in place.

Speed: Integrating processes from newly acquired agencies takes days rather than weeks because we have a standardized library of reusable agentic primitives.

Efficiency: A significant amount of repetitive operational work has been automated, allowing our teams to focus on the areas where human judgment and interaction create the most value.

Engineering leverage: Agentic development workflows materially increase the speed and quality at which our engineering team can build, test, and evolve software.

Technology Stack

TypeScript (Primary) · Node.js · PostgreSQL · LLM Orchestration · Agentic Workflows

Why This Might Not Be for You

This role is probably not the right fit if:

You primarily want to research new models, read the latest papers, or train custom models. We are pragmatic builders rather than an AI research lab.

You prefer working from a clearly defined backlog where someone else determines what should be built.

You enjoy prototyping AI systems but are less interested in the less glamorous work required to make them reliable — evaluation, logging, error handling, observability, infrastructure, and operational edge cases.

You see AI primarily as a feature rather than a new way of designing and building software systems.

Compensation and benefits

The role is fully remote, providing flexibility and enabling seamless collaboration with our geographically distributed team

Competitive salary with the potential for equity options based on performance, recognising exceptional contributions to our integration success

What is it like being a Dwell-er

Feel free to check out Dwelly Core Principles . That’s about what we believe in, how w