Who we are:
We are a rapidly growing pan-European digital wealth manager, serving over 167,000 active investors with more than £5.5 billion invested on our platform. We began in Milan in 2011 with the purpose to help more people improve their financial well-being by making personal investing simple and accessible through technology. Fast forward to today, and we are recognised as one of the most innovative fintechs, headquartered in the heart of London.
With a team of 220+ people across 4 offices in Italy and the UK, we are proudly backed and funded by major partners including Poste Italiane, Cabot Square Capital, United Ventures, and Allianz.
Our vision
Our vision is to combine passion, expertise, and technology to provide best-in-class investment solutions and advice that protects and grows client wealth over time.
Our Core Values:
We’ve built our business on three Principles:
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Relationships are our first asset: We’re one team, built on trust, honesty, and transparency. We value our relationships above all else.
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Trust drives success: We give each other the space to grow. We empower our employees to succeed, so they can make a real impact.
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Our customers dream big, just like us: We see the bigger picture and we make sure our customers see it, too. We’re always focused on the best outcomes for our clients and for each other, no matter what the goal, or how big the dream
What this means in practice:
At Moneyfarm, our success comes from the impact each of us makes. We move with purpose, urgency, and ambition, focused on delivering outcomes that matter for our clients and our business. Everyone is empowered to take ownership, challenge the status quo, and turn bold ideas into results. As we evolve, we embrace AI as a catalyst for sharper thinking, smarter decisions, and even greater impact.
Our diversity makes this possible. Different perspectives, backgrounds, and experiences fuel our creativity and drive better decisions — it’s our competitive edge. We value people for who they are and their unique strengths: that's why we offer flexible ways of working to support them in doing their best work.
Candidates who belong to 'Categoria protetta' (Legge 68/99) are more than welcome!
The role in one line
Design, build, and operate the shared AI/LLM platform — and the central backend services that sit behind it — so Moneyfarm's product teams can ship AI-powered features across investing, pensions, and share dealing, from guidance today toward fully personalised, regulated advice.
How the team works
The AI team is small (five people) and tightly coupled. You'll work alongside the AI Stream Lead as the senior engineering counterpart on the team. The Stream Lead owns prioritisation, cross-team strategy, capacity allocation, and final buy-vs-build calls. You own how the AI stack is actually built and operated, and you're the technical voice in those strategic decisions. Many things will be co-decided in practice — strategy and engineering are tightly coupled here, and we want it that way.
This is a hands-on role. As well as designing the platform, you'll write the central backend services that power our AI projects — APIs, data access layers, integration points between LLMs and our internal systems. We're not hiring an architect who delegates the build.
Three embedded engineers ship AI features inside the product teams (we call them "bubbles" and include tech, product, marketing and all other functions required to run that vertical). You set the technical standards they work to and review their work; the Stream Lead allocates their capacity.
Our environment
Moneyfarm's backend runs on event-driven microservices, with Scala as a primary language across much of the platform. The AI team's own services will be built in TypeScript and Python, but you'll integrate with — and sometimes touch — Scala services. Comfort with functional programming and event-driven patterns is part of working effectively here.
Responsibilities:
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The shared AI stack: LLM orchestration, retrieval, prompt management, evaluation, guardrails, observability
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Central backend services that drive AI projects — building them yourself, in TypeScript
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Production reliability, latency, and cost of AI systems running in the firm
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Access and data governance patterns for LLM use (e.g. how an LLM safely accesses CRM or client data)
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Engineering standards and technical review for embedded engineers shipping AI features in Bubbles
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Technical recommendations on tools, frameworks, and vendors — feeding into buy-vs-build decisions made with the Stream Lead
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Partnering with Compliance on technical safety and evaluation patterns as scope progresses toward regulated advice
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What you'll work on in the first 6–12 months
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Standing up the production LLM platform Moneyfarm's product teams will build on
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Building the central backend services that the platform and Bubble integrations depend on
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Shipping the first wave of AI features across investment guidance and onboarding
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Establishing evaluation and safety patterns that hold up under FCA/Consumer Duty scrutiny
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Setting the engineering bar and review patterns for embedded developers as they come online
Requirements
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Must-haves
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Shipped and operated LLM-based features in production
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Strong hands-on backend engineering — you'll write production services, not just design them
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Fluent in TypeScript (both, not either)
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Familiarity with functional programming — Ideally in Scala
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Strong knowledge of software architecture design patterns
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Experience designing or operating event-driven architectures (Kafka, event sourcing, async messaging patterns)
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Hands-on with orchestration frameworks, RAG, evals, and guardrails
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Thinks clearly about identity, access, and data governance — especially when LLMs touch sensitive systems
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Pragmatic on cost/latency trade-offs and model selection
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Comfortable being the technical voice in strategic conversations and pushing back when needed
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Fluent in the AI tooling landscape — can compare options quickly and credibly
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Nice-to-haves
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Experience in regulated industries (financial services, healthcare, legal)
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Familiarity with Python
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Familiarity with European data/privacy frameworks (GDPR, FCA expectations)
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Working knowledge of Scala specifically (you'll integrate with our Scala services)
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Mobile or full-stack range (the broader product is web + mobile)
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Previous role in a small, high-trust team where the engineer shaped technical direction directly
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Not a fit if
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Background is primarily ML research, model training, or data science
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Looking for an architect/lead role where someone else writes the code
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Wants a fully greenfield environment with no regulatory or legacy constraints
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Prefers strict role boundaries — this is a small team where engineering and strategy overlap by design
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Logistics
For this role, please upload your CV in English.
