MLOps Engineer - Contract (London, Hybrid)

Space Executive; London; Hybrid; GBP 125,000 - 150,000; Full time

Job summary

A global fintech company is looking for an MLOps Engineer in London to architect robust processes for ML models. The role combines technical expertise with practical implementation, ensuring seamless integration between research and production environments. Interested candidates with a strong background in Python and DevOps principles are encouraged to apply.

Qualifications

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

Responsibilities

• Architect robust processes to streamline and maintain ML models.
• Bridge the gap between research and deployment in production.
• Ensure the platform delivers transformative results for development partners.

Skills

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.

It is a hybrid role and you will need to be in their London office 3 days per week (Tuesday to Thursday). Regarding daily rate, flexibility is on offer, and the company is still determining whether this is inside or outside IR35.

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 their 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 machine learning models in a dynamic production environment. You'll bridge the gap between research and deployment, ensuring the platform continues to deliver transformative results for 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.