Senior Backend Engineer

Senior Backend Engineer - London or England remote

London, UK · Permanent · Hybrid

Join a team small enough that what you build is what exists, and take agentic AI features from a founder's idea to something hundreds of investment firms rely on to make real capital decisions.

What you'd actually work on

• Building and shipping production backend services and APIs in Python (FastAPI) that power the platform's AI features, used daily by professional investors

• Designing and owning the orchestration layer for agentic AI capabilities: how the system plans, calls tools, and retrieves the right data at the right time

• Working directly with PostgreSQL and ClickHouse to enable fast, reliable data retrieval for AI-driven analysis at real scale

• Taking a feature from a founder's rough idea to something live in production within the same sprint, without layers of process in between

• Mentoring teammates and helping set engineering standards as the team grows from around seven engineers today to over thirty

• Making real calls on architecture and tooling rather than inheriting decisions frozen in place years ago

• Working closely enough with the founders that your technical judgment shapes what gets built next, not just how it gets built

Where it gets technically interesting

• Agentic workflows and tool use sit on top of a data platform that has to stay fast and correct under real investor usage, not a demo

• Two different database workloads in the same system: PostgreSQL for transactional data, ClickHouse for the columnar, analytics-heavy queries behind the AI insights

• AI coding tools (Cursor, Claude) are part of the actual day-to-day workflow, not a side experiment

• A genuinely small engineering team means no one else is quietly maintaining the parts you don't touch

• Retrieval and embeddings work that has to hold up against messy, high-stakes private market data rather than clean public datasets

What we're looking for

• 5 to 15 years of backend engineering experience, most of it in Python, in production environments

• A track record of building and personally owning APIs or services used by real customers, not just maintaining systems someone else designed

• Time spent at a startup or scaleup (roughly 30 to 100 people), not exclusively large corporate environments

• Hands-on experience shipping AI or LLM features to production: agentic workflows, RAG, or tool use

• Strong SQL and solid experience with PostgreSQL or an equivalent relational database