MACHINE LEARNING OPERATIONS ENGINEER

Job purpose

The ML Ops Engineer operates the shared technology and release processes that keep AI models and services reliable, secure and supportable. The role automates controlled deployment, monitoring and recovery, manages approved AI platform and cloud-service usage, and supports production operations with ICT and Cybersecurity. It enables model teams to release approved solutions consistently while preserving change control, traceability and operational accountability.
Principal responsibilities
Operate approved AI platform environments and document service ownership, dependencies, capacity and support arrangements with ICT.
Automate build, test and release workflows, preserving separation between development, testing and production environments.
Manage model registries, approved deployment artefacts and environment configuration with versioned release and approval records.
•

Deploy model packages supplied by ML Engineers only after required validation, governance and change-management conditions are evidenced.
•

Implement release checks, controlled rollout, rollback and recovery procedures; test them before production changes.
•

Configure platform access, service identities and secrets under approved policies with ICT and Cybersecurity; avoid shared or embedded credentials.
•

Maintain monitoring and alerting for service availability, latency, failures, resource consumption and model-monitoring data delivery.
•

Route model-quality alerts to Data Scientists and Model Owners while owning platform and deployment fault diagnosis within assigned responsibilities.
•

Manage approved AI cloud-service usage, quotas and cost allocation; flag unusual consumption and optimise spend within agreed service needs.
•

Coordinate patching, supported software versions and vulnerability remediation with ICT, Cybersecurity and platform suppliers.
•

Support incidents through triage, recovery, root-cause analysis and corrective actions; preserve logs and escalate business-impacting events.
•

Maintain and test backup, restore and disaster-recovery arrangements against approved service requirements with ICT and continuity owners.
•

Produce operational runbooks and handover evidence; report availability, release failures, recovery time, capacity and usage costs.
•

Retire approved endpoints and resources securely, preserving required records and removing access only through authorised change processes.
•

Implement and support CI/CD pipelines, automated testing, deployment automation, and Infrastructure-as-Code (IaC) practices to improve the reliability, consistency, and efficiency of AI solution delivery.
•

Administer and maintain model deployment environments, model registries, experiment-tracking platforms, and operational tools supporting the machine learning lifecycle.
•