Machine Learning Operations Engineer

CMC Markets; Greater London; On-site; GBP 65,000 - 100,000; Full time

Benefits offered by this job

Pension

Income Protection

Life Assurance

Health Insurance

Holiday 25 days

Breakfast provided

Discretionary Bonus

Job summary

CMC Markets is hiring an ML Ops Engineer to build and operate platform capabilities that move machine-learning models from experimentation into reliable production services.

You will own automation, deployment, observability and operational controls around the ML lifecycle, collaborating with research, software, platform and product teams to ensure reproducibility, scalability, security and dependability of ML systems.

Qualifications

• 3-7 years of professional experience in MLOps, ML platform engineering, or related fields.
• Experience designing CI/CD workflows for ML or production software.
• Hands-on experience with workflow/orchestration systems for ML training, validation or deployment.
• Strong production Python skills with clean APIs and maintainable services.
• Experience deploying and operating ML models in production environments.

Responsibilities

• Build repeatable workflows for model training, validation, promotion, deployment and retraining.
• Deploy and operate batch and online inference services in containerised cloud environments.
• Design and implement CI/CD pipelines for ML systems with automated testing and release controls.
• Manage experiment tracking, model metadata and reproducibility across research and production.
• Establish dashboards, alerts, and runbooks for incident detection and resolution.

Skills

Python programming

Observability

CI/CD

ML lifecycle

Container orchestration

Security & reliability

Experiment tracking

Model serving

Automation

Tools

PyTorch

Model registry

Containers

Infrastructure as code

CI/CD tooling

Job description

• 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 Insurance provided through Vitality
• Holiday: 25 days - per calendar year + additional day off for your birthday
• Breakfast provided for office days
• Discretionary Bonus

Comfort working with cloud infrastructure, containers, infrastructure as code and service networkingClear communication skills and the ability to work effectively with research, engineering, platform, security and product teamsAbility to reason about system design, reliability and operational trade-offs—not just individual toolsHands-on experience with at least one workflow or orchestration system used for ML training, validation or deploymentExperience designing CI/CD workflows and release processes for ML or other production software systemsExperience deploying, serving and operating machine-learning models in production environments3-7 years’ professional experience in MLOps, ML platform engineering, ML infrastructure, backend engineering, DevOps or SREStrong understanding of observability, monitoring, alerting, incident response and common failure modes in ML systemsStrong production Python skills, including clean APIs, testing, performance awareness and maintainable servicesPractical understanding of the ML lifecycle, including training, validation, inference, model release, monitoring and retrainingExperience with PyTorch or similar ML frameworks and model-serving technologiesExposure to regulated or high-reliability production environmentsExperience supporting multiple models, services or teams on a shared ML platformFamiliarity with model registries, feature stores and offline/online feature-consistency challengesPrior ownership of model monitoring, drift detection or automated retraining