Production ML Engineer: Reliability & Scale

CMC Markets; City Of London; On-site; GBP 85,000 - 125,000; Full time

Job summary

CMC Markets is seeking an ML Ops Engineer to build reliable production services for machine learning models. This hands-on role focuses on deployment, automation, monitoring and controls across teams to ensure models are reproducible, secure and scalable.

You will implement repeatable workflows, containerised deployments, and observability across ML lifecycles, working with research, software, platform and product teams to move ideas from experiments to live production.

Qualifications

• 3–7 years in ML Ops or related backend/infrastructure roles.
• Strong Python production skills with API design.
• Experience deploying ML models to production environments.
• Familiarity with ML lifecycle: training, validation, inference, monitoring.
• Experience with CI/CD and workflow orchestration.

Responsibilities

• Build repeatable workflows for training, validation, release, deployment and retraining.
• Package and version models, integrate registries, automate deployments and safe rollbacks.
• Create CI/CD pipelines with automated testing, validation and release controls.
• Build reusable tools and platform capabilities for multiple models.
• Deploy and operate batch and online inference services in containers.
• Monitor service health, data quality, drift and model performance with dashboards and alerts.
• Investigate production issues across models, apps, infra and data dependencies.
• Write production-grade Python for services and ML workflows.
• Collaborate with platform, security, data engineering and product teams on inputs and access controls.

Skills

Python

CI/CD

Cloud infra

Containers

Model deployment

Observability

MLOps

Tools

PyTorch

Job description

CMC Markets is seeking an ML Ops Engineer to build reliable production services for machine learning models. This hands-on role focuses on deployment, automation, monitoring and controls across teams to ensure models are reproducible, secure and scalable.

You will implement repeatable workflows, containerised deployments, and observability across ML lifecycles, working with research, software, platform and product teams to move ideas from experiments to live production.