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.

Principle 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.
• Collaborate with ML Engineers, Data Scientists, AI Platform Engineers, ICT, and Cybersecurity teams to support the deployment, operation, monitoring, and continuous improvement of AI services.
• Monitor platform performance, service health, capacity utilization, operational risks, and reliability metrics, and coordinate remediation activities where required.

Qualifications Required

• Bachelor's degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, Machine Learning, Data Engineering, Cloud Computing, Information Systems, Cybersecurity, or a related field.
• A Master's degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Data Engineering, Cloud Computing, Information Systems, or a related field will be an added advantage.
• Professional certifications in DevOps, Cloud Computing, MLOps, Platform Engineering, Container Platforms, Site Reliability Engineering, Cybersecurity, or related disciplines will be an added advantage.
• Experience working with Data Scientists, ML Engineers, Data Engineers, Platform Engineers, ICT, and Cybersecurity teams in delivering AI and advanced analytics solutions.
• Relevant DevOps, cloud, container-platform or machine learning operations certification is an advantage.
• Minimum of 3 years of experience in DevOps, MLOps, platform engineering, cloud engineering, site reliability engineering, or production operations, including support for machine learning, AI, analytics, or data workloads.
• Skills in Python or shell scripting, Linux, containers, automated delivery pipelines and infrastructure configuration.
• Experience with monitoring, access control, incident response, backup and recovery.
• Ability to manage service consumption, reliability measures and technical support documentation.
• Knowledge of machine learning lifecycle management, model deployment processes, model registries, experiment tracking, model monitoring, and MLOps best practices.
• Experience with observability, monitoring, logging, alerting, performance management, capacity planning, and operational support practic

• 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.
• Collaborate with ML Engineers, Data Scientists, AI Platform Engineers, ICT, and Cybersecurity teams to support the deployment, operation, monitoring, and continuous improvement of AI services.
• Monitor platform performance, service health, capacity utilization, operational risks, and reliability metrics, and coordinate remediation activities where required.

• Python or shell scripting
• Linux
• containers
• automated delivery pipelines
• infrastructure configuration
• monitoring
• access control
• incident response
• backup and recovery
• service consumption management
• reliability measures
• technical support documentation
• machine learning lifecycle management
• model deployment processes
• model registries
• experiment tracking
• model monitoring
• MLOps best practices
• observability
• logging
• alerting
• performance management
• capacity planning
• operational support practices

• Bachelor's degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, Machine Learning, Data Engineering, Cloud Computing, Information Systems, Cybersecurity, or a related field.
• A Master's degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Data Engineering, Cloud Computing, Information Systems, or a related field will be an added advantage.
• Professional certifications in DevOps, Cloud Computing, MLOps, Platform Engineering, Container Platforms, Site Reliability Engineering, Cybersecurity, or related disciplines will be an added advantage.
• Experience working with Data Scientists, ML Engineers, Data Engineers, Platform Engineers, ICT, and Cybersecurity teams in delivering AI and advanced analytics solutions.
• Relevant DevOps, cloud, container-platform or machine learning operations certification is an advantage.
• Minimum of 3 years of experience in DevOps, MLOps, platform engineering, cloud engineering, site reliability engineering, or production operations, including support for machine learning, AI, analytics, or data workloads.
• Skills in Python or shell scripting, Linux, containers, automated delivery pipelines and infrastructure configuration.
• Experience with monitoring, access control, incident response, backup and recovery.
• Ability to manage service consumption, reliability measures and technical support documentation.
• Knowledge of machine learning lifecycle management, model deployment processes, model registries, experiment tracking, model monitoring, and MLOps best practices.
• Experience with observability, monitoring, logging, alerting, performance management, capacity planning, and operational support practic