Backend Engineer with strong applied ML experience

About Dwelly

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.

Position Summary

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.

What You’ll Do

• 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.

What We’re Looking For

• 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. 

Nice to Have

• 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.

What Success Looks Like

• 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.

Compensation & Benefits:

• Fully remote role.
• Competitive compensation based on experience and impact.
• Opportunity to work on high-leverage automation systems at the intersection of backend engineering, applied ML, data and real operational workflows.
• Competitive salary with the potential for equity options based on performance, recognising exceptional contributions to our integration success.