Job Brief: Lead AI Engineer (Stealth Fintech Start-Up)

Overview

A high-growth fintech start-up in stealth mode is building a personal finance app — a next-generation AI-powered personal finance coach that helps users better understand and optimise their spending habits. The app leverages open banking, LLMs, and custom AI tooling to deliver actionable insights, smart budgeting recommendations, and personalised cashback offers — all tailored to each user’s unique financial behaviour.

To get ready for their launch, they are looking to hire a Lead AI Engineer to own and evolve the AI capability, taking the product from solid MVP to an intelligent, scalable, and market-ready platform.

This is a fully remote opportunity (UK-based), with occasional travel to London for company meetups.

The Role

As the Lead AI Engineer, you’ll be responsible for:

• Driving the technical evolution of the company’s proprietary financial LLM coach.
• Overseeing architecture and optimisation of the AI backend, including vector databases and LLM integrations.
• Ensuring smooth interaction between the AI layer and the app’s codebase (front-end and back-end).
• Leading and mentoring a small but growing team (initially one mid-level AI engineer).
• Collaborating closely with the product and leadership team to push the boundaries of conversational AI and personalised finance.

This is a hands-on leadership role — ideal for an experienced AI engineer with strong software development foundations and an appetite for ownership in a fast-moving start-up environment.

Tech & Tools

You’ll be working with:

• LLMs: ChatGPT, Gemini, and other models via a switching system.
• Database: Supabase and vector databases for memory and user data handling.
• Backend & Infrastructure: Python, Node.js, or equivalent.
• Frontend Collaboration: Understanding of app-side integration (React Native or similar).
• Prompt Templates and mathematical tools built into chat logic.

Key Responsibilities

• Own end-to-end delivery of AI-driven product features, from concept through to deployment.
• Enhance the app’s “financial coach” by improving memory, contextual awareness, and response accuracy.
• Build and manage tools that enable personalised recommendations and insights based on user spending data.
• Ensure strong communication between the AI and product layers, including UI interactions (e.g. chat triggers opening app features).
• Recruit, mentor, and lead a small team of prompt engineers and developers.
• Collaborate with leadership on AI strategy, architecture decisions, and roadmap planning.

Requirements

Essential:

• Proven experience in AI/ML engineering, particularly with LLM implementation and optimisation.
• Solid software development background — comfortable working across both backend and frontend environments.
• Experience integrating LLMs into consumer applications.
• Strong command of English language (must understand tone, nuance, and conversational context).
• Experience working in start-up or product-led environments.
• Deep understanding of how AI and app experiences intersect.

Desirable:

• Fintech or open banking experience.
• Familiarity with Supabase, vector databases, and multi-LLM orchestration.
• Leadership experience or interest in growing and managing a small AI function.
• Understanding of prompt engineering best practices.

Culture & Fit

We’re looking for someone who:

• Enjoys autonomy and can balance technical depth with creative problem-solving.
• Can communicate clearly and understands tone and nuance in human language (our app is chat-led and personality matters).
• Is comfortable working remotely while being collaborative and proactive.
• Wants to be part of a mission-led company aiming to make personal finance simpler and more empowering for everyone.

Compensation

• Salary range: £80,000 – £120,000, depending on experience.
• Equity available for the right candidate.
• Remote-first with occasional London meetups (a few times per year).

Interview Process

1. Intro & Culture Fit Interview – get to know the team and assess alignment.
2. Technical Task – an LLM-based challenge to demonstrate problem-solving ability.
3. Final Interview – discussion around leadership approach, team structure, and future vision.