CMC Markets
ML Ops Engineer
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ML Ops Engineer
Build the systems that make machine learning reliable in production
CMC Markets is looking for an MLOps Engineer to build and operate the platform capabilities that take machine-learning models from experimentation into reliable production services.
You’ll work at the intersection of ML infrastructure and software engineering, owning automation, deployment, observability and operational controls across the ML lifecycle. Working closely with research engineers, software engineers, platform teams and product teams, you’ll help make models reproducible, scalable, secure and dependable, from packaging and release through to serving, monitoring, retraining and incident response.
This is a hands-on engineering role, not a research position. You’ll help turn promising experiments into production systems with clear SLAs, observable behaviour and the reliability required to operate at scale.
What you’ll do
- Build repeatable ML workflows for training, validation, promotion, deployment and retraining.
- Productionise models through packaging, versioning, model registry integration, deployment automation and safe rollback.
- Design CI/CD pipelines with automated testing, validation and release controls, while managing experiment tracking, model metadata and reproducibility.
- Build reusable tooling and platform capabilities for multiple models and engineering teams.
- Deploy and operate batch and online inference services in containerised cloud environments, with clear availability and latency objectives.
- Monitor service health, infrastructure, data-quality signals, data drift, prediction drift and model performance using dashboards, alerting and operational runbooks.
- Debug production issues across model, application, infrastructure and critical data-dependency layers, improving robustness through automation, observability and infrastructure as code.
- Write production-grade Python for long-running services, deployment tooling and ML workflows.
- Collaborate with platform, security, data engineering and product teams on model inputs, access controls, secrets, resilience and compliance.
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What you’ll bring
- 3–7 years’ professional experience in MLOps, ML platform engineering, ML infrastructure, backend engineering, DevOps or SRE.
- Strong production Python skills, including clean APIs, testing, performance awareness and maintainable services.
- Experience deploying, serving and operating ML models in production, with practical knowledge of training, validation, inference, release, monitoring and retraining.
- Experience designing CI/CD workflows and using workflow or orchestration systems for ML.
- Comfort working with cloud infrastructure, containers and infrastructure as code.
- Strong understanding of observability, monitoring, alerting, system design and common failure modes in ML systems.
- Ability to reason about reliability and operational trade-offs, not just individual tools.
- Clear communication skills and the ability to work effectively across research, engineering, platform, security and product 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.
- Experience with PyTorch or similar ML frameworks, model-serving technologies or regulated, high-reliability production environments.
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Technology environment
- Language: Python
- ML tooling: PyTorch or similar frameworks, experiment tracking and model registries
- Workflow orchestration: ML workflows for training, validation, deployment and retraining
- Deployment: Containers, model-serving frameworks and infrastructure as code
- Observability: Metrics, logging, tracing, alerting and monitoring across model, service and platform layers
- Cloud: Managed compute, storage and networking, with a provider-agnostic mindset
The technology stack will evolve. We value engineers who understand why systems are designed in particular ways and can adapt as requirements and tools change.
Why this role matters
Machine-learning models only create value when they are correct, observable and dependable in production. This role is responsible for making that happen.
You’ll reduce the gap between promising experiments and production systems that can be trusted by downstream products and customers. Your work will improve the reliability, speed and scalability of the ML platform across the organisation.
If you care about operational clarity, robust engineering and building ML systems that do not silently fail, this role gives you direct leverage over the success of our machine-learning capabilities.
CMC Markets is an equal opportunities employer and positively encourages applications from suitably qualified and eligible 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.
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