MLOps Engineer - Permanent (London, Hybrid)

Space Executive; City Of London; Hybrid; GBP 80,000 - 100,000; Full time

Job summary

A global fintech company is seeking an MLOps Engineer to streamline machine learning models. This hybrid role requires presence in the London office three days a week and offers a market-leading salary. Candidates should have strong Python skills, experience in ML systems, and a DevOps mindset to succeed in this innovative environment.

Qualifications

• Proven experience building and maintaining operational ML systems.
• Strong Python coding skills with ML/data engineering libraries.
• Understanding of CI/CD principles and containerisation.

Responsibilities

• Architect robust processes for machine learning models.
• Bridge the gap between research and deployment.
• Ensure platform delivers transformative results.

Skills

ML in Practice

Python Proficiency

DevOps Mindset

Problem-Solving Approach

Tools

Docker

Kubernetes

Git

Job description

My client, a global fintech company disrupting the world of embedded finance, is seeking a MLOps Engineer to join their team.

This is paying a market leading salary + benefits. It is a hybrid role and you will need to be in their London office 3 days per week (Tuesday to Thursday).

Company overview

A global embedded financing platform for many of the world’s leading e-commerce sites, tech companies and payment services. Their software platform and APIs enable our partners to offer flexible financing products, in their desired branding, to their merchant base.

Role overview

As an ML Ops Engineer, you'll be the architect of robust processes to streamline and maintain our machine learning models in a dynamic production environment. You'll bridge the gap between research and deployment, ensuring our platform continues to deliver transformative results for our development partners.

Requirements

• ML in Practice: Proven experience building and maintaining operational ML systems.
• Python Proficiency: Strong Python coding skills and familiarity with relevant ML/data engineering libraries.
• DevOps Mindset: Understanding of CI/CD principles, containerisation (Docker, Kubernetes), and version control (Git).
• Problem-Solving Approach: Ability to troubleshoot issues in complex ML pipelines and proactively address potential bottlenecks.