Software Engineer (AI Systems) - Ledgy Agent & AI Platform

At Ledgy, we’re on a mission to make Europe a powerhouse of entrepreneurship by building a modern, tech-driven equity management and financial reporting platform for private and public companies. In 2026, we aim to be the leading provider for European IPOs and reporting for share-based payments.

As a Software Engineer on our AI Systems team, you’ll build the AI foundations and product experiences of Ledgy: the Ledgy Agent, the tools and skills it runs on, our MCP server, and the platform capabilities that let other teams embed AI safely in their domains. You’ll collaborate with fellow engineers, product managers, and designers in a new and fast-moving product area where correctness matters as much as speed—our customers need equity data they can trust.

Location: We hire remotely across the UK, Spain, Germany, Poland, and Romania. For candidates based in London, we use a hybrid model with 2 days in the office per week.

The team you’ll join: AI Systems – Develops Ledgy’s AI foundations and product experiences, from AI agents and AI-assisted workflows to the platform capabilities that help other teams embed AI safely in their domains. The team works in a new and evolving product area, balancing rapid iteration with reliable execution, evaluation tooling, and responsible automation.

What You’ll Work On

Help build the future of how companies manage equity.

• Ledgy Agent. You’ll design and build the system behind our in-app agent, so equity administrators can do through AI what they do manually in the app today. Ledgy Agent takes real actions and runs real analysis on live equity data, like onboarding a stakeholder end-to-end, and administrators depend on its accuracy and observability. You’ll design the tools, skills, context, and orchestration behind it, and how it decides to handle each request.
• Ledgy MCP. Not everyone wants to open Ledgy to get things done. Our MCP server lets customers bring equity into the AI agents and tools they already use. You’ll shape it alongside the in-app agent, so a capability built once works in both places, and you’ll decide what we expose and how we keep it safe.
• Measuring and improving the agent. You’ll design and build the system we use to measure the agent’s quality and learn from how customers use it: evaluations, tracing, and feedback from real conversations. You decide what “good” means for customers, how we measure it, and how what we learn changes what we build next.
• Scaling. The Ledgy web application, the agent, MCP, and surfaces we haven’t built yet all need the same capabilities. You’ll design the foundations that let us build AI features once and reuse them everywhere, and that let other teams add AI to their domains without compromising correctness. You’ll also design how it runs as usage grows: the backend infrastructure, cost, and latency behind the agent and MCP, and how we keep them reliable and observable.

You’ll own real parts of this and have a say in where we invest. You’ll also spend time with customers to see how administrators actually use the agent, and use that to decide what to build next.

You’ll Thrive in This Role If You

Don’t worry if you don’t tick every box. These are the things that would make you an especially good fit.

• You have 5+ years of experience working as a Software Engineer, with strong TypeScript/Node fundamentals across back end and front end.
• You’ve built and shipped agentic or LLM-based software to production, such as an agent, an assistant, or a chat product, and you’ve been the one running it. You’ve debugged failures in real conversations and kept cost and latency in check as usage and models changed.
• You’ve designed the agent itself: tools and skills, context and prompt engineering, state, streaming, and the agent loop. You know at least one agent framework or runtime well and can say where it works and where it doesn’t.
• You improve these systems with evidence. Evals, regression sets, tracing, and feedback from real usage tell you whether a change helped, and you use them to decide what to do next.
• You keep up with what the foundation labs and the wider community are learning about building agents. You try new techniques early and judge which ones make our product better and are worth adopting now, and which should wait.
• You take part in deciding what we build. You read the data, talk to customers, and push for what you think matters most.

Our Tech Stack

We work in a TypeScript monorepo: React and Tailwind on the front end, tRPC, Node.js, MongoDB, and Temporal on the back end. It runs on GCP with Kubernetes, managed through Terraform. The agent is currently built on the Vercel AI SDK, and we run an MCP server alongside it.

You don’t need to know all of it, but hands-on experience building with LLMs is strongly preferred.

What Your Journey Could Look Like

First day: Get the app running locally and make your first commit. Your onboarding buddy helps you get there.

First week:

• Meet the engineers, product managers, and designers you’ll work with.
• Learn the agent end to end: its tools, skills, MCP surface, and how we evaluate it.
• Ship a few smaller features.

First month:

• Meet the wider engineering team and learn how Ledgy works and how customers use it today. This is the background you’ll use for decisions later.
• Make larger contributions and take a bigger part in code reviews.

First 3 months:

• Drive part of a larger piece of work, such as process, tooling, a new product, or new functionality.
• Work with Product and Design to prove and deepen the value AI adds to the product.
• Take part in architecture and long-term decisions.

After the first 3 months:

• Take a bigger role in shaping where the team is heading. Own key parts of our AI platform and decide where we can add the most value for customers.
• Mentor other engineers and raise the bar. Help teams across Ledgy add AI to what they build, whether that means advising, reviewing, or working alongside them.
• Help shape Ledgy’s technical direction, for AI and for everything else we build.

The interview process:

• Getting to know you (45 min): Meet with someone from our people team to talk about your experience and to ask questions about the role.
• Live Coding (60 min): An equity domain problem to solve collaboratively using your preferred language and IDE. We want to see how you think, so this step does not allow AI. But you can freely look up syntax questions if you need to.
• System Design (60 min): A system design session where you will design an architecture for a problem from the Ledgy domain using a visual tool of your choice.
• Behavioural (45 min): Answer behavioural style questions about your experience, using the STAR method—with a PM and EM.
• Culture Interview: A chance to meet one of our founders and your hiring manager for a 30-minute chat about Ledgy’s mission and values.
• Offer 🎉

Being part of Ledgy means:

• Recharge and re-energize with flexible working hours, 25 days of vacation, and up to 40 days of remote work from outside your home country.
• We make space for your own professional development, with a generous yearly learning & development budget.
• This role will receive a competitive salary + benefits + equity. All salaries are aligned with competitively benchmarked ranges based on work location and levelling.
• For candidates based in London, we use a hybrid work model of 2 days in the office per week.