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Dwelly is building an AI operating system for residential lettings, operating over 15,000 properties and $470M in GMV as one of the UK's ten largest lettings operators. The company has raised $263M and is backed by top-tier investors.
You will join as an early core member of the AI infrastructure team, tasked with building the agentic systems that power automation across Dwelly's rapidly expanding network of acquired agencies. This is production-focused engineering, not research.
Key responsibilities include:
**Agentic Infrastructure**: Design and build core primitives for agentic systems—memory, context management, tool-calling, orchestration, and feedback loops. Move from one-off AI solutions toward reusable infrastructure that enables rapid, reliable workflow automation as the company scales.
**Evaluation & Observability**: Establish frameworks to understand agent performance, success, and failure modes. Build testing, tracing, debugging, and evaluation systems as fundamental to the engineering process as unit testing in traditional software.
**Agentic Development**: Push forward how LLMs are used to build software. Create workflows where coding agents and specialized subagents explore repositories, implement changes, review architecture, run evaluations, and iterate with minimal human coordination.
**Orchestration**: Design systems where specialized agents, tools, deterministic software, and humans work together effectively. Make architectural decisions about when to use LLMs, traditional software, or coordinated multi-agent approaches.
**Operational Automation**: Bridge complex operational workflows and engineering. Work with operational and product teams to identify high-leverage automation opportunities and turn them into reliable production systems.
**Architectural Influence**: Help define architectural patterns for agentic systems as the company scales. You will have significant influence over technical approach, tooling, and engineering standards.
Required qualifications: strong software engineering background with experience delivering complex systems end-to-end; hands-on production experience building AI/agentic systems; deep understanding of reliability, latency, context management, failure modes, evaluation, and observability; advanced practical use of modern LLMs; experience with coding agents and agentic workflows; strong architectural judgment; TypeScript/Node.js or Python experience; high autonomy and ownership; strong product judgment; fluent English; startup mentality.
The role explicitly does not require ChatGPT usage, single LLM API calls, or prompt wrappers—it seeks engineers who think in terms of agents, tools, context, orchestration, evaluation, and human-machine collaboration.
