Production ML Ops Engineer

CMC Markets; United Kingdom; On-site; GBP 90,000 - 130,000; Full time

Job summary

CMC Markets is seeking an ML Ops Engineer to transform machine learning models into reliable production services. This hands-on role focuses on deployment, automation, monitoring and operational controls across the ML lifecycle.

You'll collaborate with research, software, platform, and product teams to ensure reproducibility, security and scalability, delivering observable, resilient ML systems at scale for the business.

Qualifications

• 3-7 years' experience in ML Ops, ML platform or infrastructure engineering, or backend engineering.
• Strong production Python skills, including API design, testing and maintainable services.
• Experience deploying and operating ML models in production.
• Familiarity with ML lifecycle, from training to retraining.
• Experience with CI/CD and workflow orchestration.
• Cloud infrastructure, containers and infrastructure as code knowledge.
• Observability and failure mode awareness in ML systems.

Responsibilities

• Build repeatable workflows for model training, validation, release, deployment and retraining.
• Package and version models, automate deployments and support safe rollbacks.
• Create CI/CD pipelines with automated testing, validation and release controls.
• Build reusable tools and platform capabilities for multiple models and teams.
• Deploy and operate batch and online inference services in containerised cloud environments.
• Monitor service health, data quality, drift and model performance via dashboards and runbooks.
• Investigate production issues across models, applications, infra and data dependencies.

Skills

Python

CI/CD

Cloud

Observability

Model deployment

Back-end

Tools

PyTorch

Kubernetes

Docker

Model registry

Terraform

Job description

CMC Markets is seeking an ML Ops Engineer to transform machine learning models into reliable production services. This hands-on role focuses on deployment, automation, monitoring and operational controls across the ML lifecycle.

You'll collaborate with research, software, platform, and product teams to ensure reproducibility, security and scalability, delivering observable, resilient ML systems at scale for the business.