Machine Learning Operations Engineer, CMC Markets

• 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 machine-learning models in production environments
• Practical understanding of the ML lifecycle, including training, validation, inference, model release, monitoring and retraining
• Experience designing CI/CD workflows and release processes for ML or other production software systems
• Hands-on experience with at least one workflow or orchestration system used for ML training, validation or deployment
• Comfort working with cloud infrastructure, containers, infrastructure as code and service networking
• Strong understanding of observability, monitoring, alerting, incident response and common failure modes in ML systems
• Ability to reason about system design, reliability and operational trade-offs—not just individual tools
• Clear communication skills and the ability to work effectively with research, engineering, platform, security and product teams

Desirable

• 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