MLOps Engineer

Apron was started by a group of people who’d spent years building products for some of today’s most successful global fintech companies. But there was one problem that no-one was solving: Business money. The kind that buys tomatoes, tiles, and till rolls. The kind that keeps suppliers happy and business booming. The kind that, before you know it, eats up your entire day.

One million small businesses in the UK will each spend 5 hours this week paying and reconciling invoices, alongside countless hours chasing staff for expense receipts.

This is a problem that’s affecting entrepreneurs. Dreamers. Risk takers. Backbones of our communities. Imagine what they could do with this time instead. What would they build? How far could they go? That’s why we created Apron as an essential tech layer in the small business machine. We flip the payment experience from blocking business to boosting it. Apron weaves neatly into your workflow and tightens it up, turning hours into minutes.

We have grown fast over the past few years, expanding our team to circa 70 individuals across the UK and more. We are backed by Index Ventures, Bessemer Venture Partners, with participation of Visionaries Club and the founders of Melio and Klarna and we’ve raised $50m.

Business owners face a constant stream of tasks: invoices scattered across different sources, keeping track of what’s already been paid. On top of that, there’s bookkeeping, filing documents on time to optimize taxes, and making sure nothing slips through the cracks. When the process is set up correctly, payments run smoothly, future expenses are visible, and all the administrative work takes far less time.

We’re building a product that automates this entire workflow with AI — and we’re thrilled to see the positive feedback from café, restaurant, retail, and studio owners whose time we’ve already helped save.

We’re looking for an engineer to take ownership of our AI platform — covering model training and serving, dataset annotation, quality evaluation, and integration with external LLMs. Responsibilities include maintaining system reliability, proposing improvements, and ensuring our tools and infrastructure remain up to date.

The ideal candidate (you?) has strong software engineering experience, with a solid understanding of Machine Learning and MLOps. They should be able to identify which technologies are valuable and which add unnecessary complexity — keeping our solutions efficient, robust, and simple.

• Working on the infrastructure for AI systems that includes usage of internally trained neural networks, LLMs, embeddings, and external context usage.

• Organising models serving with high performance on high loads. Setup monitoring dashboards and alerts.

• Working on tools for model evaluation, development and serving. Dataset and models storage and versioning, reproducible models training, prompt tuning, model estimation and metrics visualisation.

• Configuring documents labelling tools and model retraining based on online feedback.

• Improving the engine for document recognition

• Ensuring data security in service and training pipelines.

• Contributing to development best practices such as tests in the team.

• Combination of experience in backend engineering, MLOps, machine learning, AI

• Extensive knowledge of Python and SQL (PostgreSQL preferred)

• Experience with cloud computing platforms (we use GCP) and containerization technologies (e.g., Docker, Kubernetes).

• Basic knowledge of machine learning algorithms, models, and statistical concepts

  It will be a plus:

  • Experience in running AB tests / AB testing platforms.

  • Experience working with Kafka, Redis

  • Knowledge of Kotlin - all backend code except ML services is written in this language.

• Highly competitive salary

• Stock options

• Health insurance with AXA (including Optical and Dental cover)

• Life Assurance with MetLife

• Enhanced parental leave

• Weekly Deliveroo allowance

• Hybrid setup, with 3 days in the office (Liverpool Street, London)

• Salary sacrifice schemes (Nursery, Cycle to Work, Electric vehicle)

• Fully expensed tech