ML Ops Engineer

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

ML Ops Engineer

Build the systems that make machine learning reliable in production.

CMC Markets is looking for an ML Ops Engineer to help turn machine learning models into reliable, scalable production services.

This is a hands‑on engineering role focused on the systems around machine learning: deployment, automation, monitoring and operational controls. You’ll work with research engineers, software engineers, platform teams and product teams to make models reproducible, secure and dependable throughout their lifecycle.

This isn’t a research role. You’ll help move models from experimentation to production, with clear service objectives, observable behaviour and the resilience needed to operate at scale.

What you’ll do

• Build repeatable workflows for model training, validation, release, deployment and retraining.
• Package and version models, integrate model registries, 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 engineering teams.
• Deploy and operate batch and online inference services in containerised cloud environments.
• Monitor service health, data quality, drift and model performance through dashboards, alerts and runbooks.
• Investigate production issues across models, applications, infrastructure and data dependencies.
• Write production‑grade Python for services, deployment tools and ML workflows.
• Work with platform, security, data engineering and product teams on model inputs, access controls, resilience and compliance.

What you’ll bring

• 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 the ML lifecycle, from training and validation through inference, monitoring and retraining.
• Experience with CI/CD and workflow orchestration.
• Comfort working with cloud infrastructure, containers and infrastructure as code.
• A sound understanding of observability, system design and common failure modes in ML systems.
• The ability to weigh reliability and operational trade‑offs, and communicate clearly across teams.

Nice to have

• Experience with model monitoring, drift detection, automated retraining, model registries or feature stores.
• Experience supporting multiple models or teams on a shared ML platform.
• Familiarity with PyTorch or similar frameworks and model‑serving technologies.
• Experience in regulated or high‑reliability production environments.

Our technology environment

We use Python, ML frameworks such as PyTorch, experiment tracking and model registry tools, workflow orchestration, containers, model‑serving technologies, infrastructure as code and cloud services. Our stack will evolve, so we value engineers who understand the principles behind these systems and can adapt as tools and requirements change.

Why this role matters

Models create value when people and products can rely on them. You’ll help close the gap between promising experiments and production systems that are observable, dependable and ready to scale, improving the reliability and speed of ML delivery across the organisation.

If you care about robust engineering and building ML systems that fail visibly and recover safely, we’d like to hear from you.

CMC Markets is an equal opportunities employer.

We welcome applications from suitably qualified candidates regardless of gender, sexual orientation, marital or civil partner status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability or age.