Applied AI Engineer

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.

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

• 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

• Remote across UK, Ireland, and European time zones
• 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.