Machine Learning Engineer

Machine Learning Engineer
Up to £70K DOE
Hybrid – London (2 days per week onsite)

My client is looking for a Junior to Mid-Level Machine Learning Engineer to take ownership of the infrastructure and services that power machine learning systems in production. In this role, you’ll act as a bridge between data science and engineering, ensuring robust, scalable, and low-latency deployment of models that serve millions of requests per day.

You’ll be responsible for building and maintaining Python microservices, leveraging modern DevOps practices and tooling to support rapid, reliable delivery. With sub-second response times and a high-throughput environment (2M+ requests/day), this is a high-impact role that blends software engineering, DevOps, and MLOps at scale.

Key Responsibilities

• Design, develop, and maintain Python microservices for serving machine learning models
  
  
• Collaborate with Data Scientists to deploy, monitor, and support models in production
  
  
• Implement and manage CI/CD pipelines using Azure DevOps
  
  
• Support containerized deployments with Kubernetes and Docker
  
  
• Ensure high performance, fault-tolerant, and secure infrastructure
  
  
• Promote code quality, testing standards, and scalable architecture
  
  
• Proactively identify infrastructure improvements and lead implementation
  
  Requirements
  
  
• 2 + years of experience in Software Engineering, DevOps, or Data Engineering
  
  
• Strong Python skills with experience in microservices and web frameworks
  
  
• Solid understanding of CI/CD, ideally using Azure DevOps
  
  
• Familiarity with containerized environments (Docker/Kubernetes)
  
  
• Exposure to Data Science or Machine Learning concepts
  
  
• Experience operating in high-throughput environments
  
  
• Independent, curious, and driven by continuous improvement
  
  
• Effective communicator with the ability to bridge data science and engineering teams
  
  Why Join?
  
  You’ll be joining a company with strong business performance and ambitious plans for data-driven growth. This is a rare opportunity to take technical ownership of real-time machine learning infrastructure and play a key role in scaling systems that make an immediate business impact