Backend Software Engineer — Applied ML & LLM Systems

Dwelly — a UK-based, AI-enabled lettings and property management platform, that is growing through a roll-up strategy acquiring estate agencies. The company leverages two arms: i) acquiring existing letting agencies, effectively buying its highly sticky, recurring revenue-type landlords portfolios, and then ii) building a top-notch technology to automate tenant management, payments, and post-rental property maintenance. The company seamlessly integrates AI services to automate all business processes within brick-and-mortar real estate agencies, integrating them into a tech-enabled digital letting platform in two months to radically improve the user experiences and increase efficiency of the business.

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.

We are looking for a Backend Engineer with strong applied ML experience to build production systems that extract, enrich, summarise and structure information from emails, documents and other unstructured data.
This is not a pure data science or research role. It is a production engineering role focused on building reliable Python backend services around NLP, retrieval and LLM-powered workflows.
You will work on practical problems such as extracting useful information from email correspondence during agency migrations and summarising a client’s full communication history inside their Dwelly profile.
The right person is comfortable working with messy real-world data, taking prototypes into production, measuring quality and improving systems through evaluation and feedback loops.

• →Build systems that extract structured data from emails, documents and other unstructured sources.
• →Enrich migrated client, landlord, tenant and property records with useful information from communication history.
• →Develop solutions that summarise a client’s full email history and surface the most relevant context inside Dwelly.
• →Build production NLP / ML-backed backend services that work reliably on messy real-world data.
• →Improve retrieval and ranking systems using approaches such as RAG, BM25, embeddings, hybrid search and reranking.
• →Define quality metrics, evaluation datasets and feedback loops for extraction, summarisation and retrieval systems.
• →Build Python backend services and APIs using frameworks such as FastAPI, Django, Flask or similar.
• →Integrate ML and LLM workflows into production systems with clear error handling, observability and maintainability.
• →Work closely with engineering, product and operations teams to turn real business problems into scalable automation systems.

• Strong Python backend engineering experience.
• Experience with API frameworks such as FastAPI, Django, Flask or similar.
• Production experience with NLP, ML, information extraction, retrieval, ranking or summarisation systems.
• Ability to take research ideas or prototypes into production.
• Strong understanding of evaluation, metrics and quality measurement for ML / LLM systems.
• Practical experience with retrieval systems such as RAG, BM25, embeddings, hybrid search or reranking.
• Comfortable working with messy, ambiguous or incomplete real-world data.
• Ability to build reliable services around ML workflows, including monitoring, testing and failure handling.
• Good understanding of LLM limitations, hallucination risks and safe user-facing AI.
• Strong ownership mindset and ability to work independently in ambiguous product areas. 

• Experience building AI or LLM agents.
• Experience with document understanding, email parsing, entity extraction or CRM enrichment.
• Experience with LLM evaluation, prompt/version management or human-in-the-loop review workflows.
• Experience with vector databases or search infrastructure.
• DevOps or CI/CD experience for deploying ML-backed services.
• Experience testing ML systems on complex production datasets.
• Experience with typed programming languages such as TypeScript, Java, C#, C++, Kotlin, Scala or similar.

• Useful information can be extracted from emails and documents with measurable quality.
• Client communication histories can be summarised safely, clearly and with relevant context.
• Retrieval and ranking systems improve over time through evaluation and feedback.
• ML and LLM workflows are reliable, observable and production-ready.
• Operations and product teams can trust the outputs and understand when human review is needed.
• Unstructured data from acquired agencies becomes usable inside Dwelly faster and with less manual work.

Feel free to check out Dwelly Core Principles. That’s about what we believe in, how we operate and make decisions.

What we offer is not a fancy office or a static workplace. Instead, this is about solving one of the world’s most complex problems in the largest consumer industry in the world: residential rentals.

More than 30% of households live in rental homes — over 5 million in the UK and more than 100 million across the EU and US. Our mission is to improve that experience through technology, automation and operational excellence.

This is about disrupting one of the largest and most antiquated industries in the world with one of the strongest operational and technical teams in the UK and Europe.

We work hard, and we aim for extremely ambitious results. We want people to be proud of what they’ve built and to one day look back and say: “Hell yeah, that was me that did it.”

• Customer obsession rather than competitive focus
• Passion for invention
• Operational excellence
• Long-term thinking