MLOps Engineer — Production ML Pipelines & Cloud

Royal Mail Group; Greater London; Hybrid; GBP 60,000 - 90,000; Full time

Benefits offered by this job

Car allowance

Healthcare

Up to 18% annual bonus potential

25 days annual leave plus bank holiday

Pension: CDC with 12%+ contributions

Job summary

Royal Mail is seeking a Machine Learning Engineer to join our Data & AI team in London Farringdon. You will help design, deploy and scale production ML systems, working with Data Scientists and Platform teams to deliver business value.

The role emphasizes modern MLOps, cloud platforms and scalable ML pipelines, with opportunities to influence enterprise-scale data products across the organisation.

Qualifications

• Proven experience as a Machine Learning Engineer or similar role delivering ML solutions into production.
• Strong Python development skills and experience with ML frameworks such as TensorFlow, PyTorch or Kubeflow.
• Experience designing, deploying and maintaining scalable ML platforms and pipelines.

Responsibilities

• Design scalable ML pipelines for training, testing, deployment and monitoring.
• Optimize model performance, reliability and scalability in production.
• Build CI/CD processes and automated workflows for ML delivery.
• Collaborate with Data Scientists to operationalise ML models and AI solutions.
• Support adoption of MLOps best practices across the data community.
• Deploy and manage ML workloads using cloud technologies.
• Ensure production APIs and ML services are resilient, secure, and scalable.
• Contribute to engineering standards and continuous improvement.

Skills

Python

TensorFlow

PyTorch

Kubeflow

Cloud experience

Tools

Kubernetes

Docker

CI/CD

Job description

Royal Mail is seeking a Machine Learning Engineer to join our Data & AI team in London Farringdon. You will help design, deploy and scale production ML systems, working with Data Scientists and Platform teams to deliver business value.

The role emphasizes modern MLOps, cloud platforms and scalable ML pipelines, with opportunities to influence enterprise-scale data products across the organisation.