ML Ops Engineer

ML Ops Engineer 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.

What you’ll work onML lifecycle and platform engineering

Build repeatable workflows for model training, validation, promotion, deployment and retraining.

Product ionise 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 roleKEY SKILLS AND EXPERIENCE3–7 years’ professional experience in MLOps, ML platform engineering, ML infrastructure, backend engineering, Dev Ops 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.

Nice to have Prior ownership of model monitoring, drift detection or automated retraining.

Familiarity with model registries, feature stores and offline/online feature-consistency challenges.

Experience supporting multiple models, services or teams on a shared ML platform.

Exposure to regulated or…