Machine Learning Operations Engineer Job London area,Greater London England UK,Software Development

Location: Greater London

We’re hiring an ML Ops Engineer to build and operate the platform capabilities that take machine-learning models from experimentation into reliable production services
You’ll own the automation, deployment, observability and operational controls around the ML lifecycle, working closely with research engineers, software engineers, platform teams and product teams
This is not a research role. It is a hands-on engineering role focused on making ML systems reproducible, scalable, secure and dependable, from model packaging and release through to serving, monitoring, retraining and incident response
ML lifecycle and platform engineering
Build repeatable workflows for model training, validation, promotion, deployment and retraining
Productionise models through packaging, versioning, model registry integration, deployment automation and safe rollback
Design CI/CD pipelines for ML systems, including automated testing, validation, release controls and environment promotion
Manage experiment tracking, model metadata and reproducibility across research and production
Build reusable tooling and platform capabilities that support multiple models and engineering teams
Model serving and observability
Deploy and operate batch and online inference services in containerised cloud environments
Define and meet availability, latency, throughput and recovery objectives for ML services
Monitor service health, infrastructure, data-quality signals, data drift, prediction drift and model performance decay
Establish dashboards, alerting and operational runbooks so failures are detected and resolved quickly
Support automated or controlled retraining, model promotion, rollback and model retirement
Debug production issues across model, application, infrastructure and critical data-dependency layers
Reliability, security and engineering quality
Improve system robustness, scalability and cost efficiency through automation, observability and infrastructure as code
Write production-grade Python for long-running services, deployment tooling and ML workflows
Establish testing, validation, release and incident-management practices for ML systems
Collaborate with platform, security and data engineering teams on reliable model inputs, access controls, secrets, resilience and compliance
Make explicit trade-offs between research flexibility, delivery speed, operational risk and production stability
Additional responsibilities
Maintain personal/professional development to meet the changing demands of the role, including all relevant regulatory and legislative training
When dealing with all customers, clients or colleagues ensure that we provide a clear, fair and consistent high quality service that presents a professional and positive image of CMC Markets
Take all reasonable steps to ensure appropriate confidentiality
Undertake such other duties, training and/or hours of work as may be reasonably required and which are consistent with the general level of responsibility of this role
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

Benefits

Pension: 9% = 5% company contribution and 4% self-contribution
-CMC Markets will match up to 7%
Income Protection
Life Assurance: 4 x salary
Health…