Job Purpose

The AI Platform Engineer builds and maintains the shared technology foundation used to develop, test and run the Bank’s AI solutions. The role configures approved computing environments, platform services, access and connectivity with ICT and Cybersecurity, and provides reusable tools for model and engineering teams. It focuses on platform capability, security, capacity and resilience, working with ML Ops on service operation and with ML Engineers on solution requirements

Principle Responsibilities

• Translate approved AI workload requirements into platform designs, capacity estimates and implementation plans with ICT and architecture owners.
• Build and configure approved compute, storage, networking and platform services for development, testing and production environments.
• Create reusable, version-controlled infrastructure configurations and environment templates to reduce manual setup and configuration drift.
• Integrate identity, role-based access and service accounts with approved bank controls; separate user, administrator and workload permissions.
• Implement approved connectivity, encryption and secrets-management controls with ICT and Cybersecurity; retain configuration evidence.
• Configure shared tools for development, experiment tracking, artefact storage and model serving, with clear service ownership and support boundaries.
• Provide approved access to generative AI services with usage limits, logging and data-handling controls aligned to permitted business use.
• Assess platform compatibility, scalability and performance under expected workloads; document constraints and recommend capacity improvements.
• Build platform health metrics and alerts for resource use, availability and infrastructure faults; hand over operational monitoring to ML Ops.
• Test platform upgrades, patches and dependency changes in controlled environments before authorised release with ICT and ML Ops.
• Engineer and test backup, restore and resilience arrangements against approved service requirements with ICT and continuity owners.
• Resolve platform defects and supplier issues, supporting incident diagnosis while ML Engineers address solution code and Data Scientists address model behavior.
• Maintain platform architecture, configuration records, runbooks and handover evidence; support security review and independent assurance requests.
• Report platform capacity, reliability, provisioning time and cost; recommend improvements and retire resources through approved change processes.
• Implement and maintain automated infrastructure provisioning, configuration management, and platform deployment processes to improve consistency, efficiency, and scalability across AI environments.

Qualifications Required

• Bachelors degree in Computer Science, Information Technology, Software Engineering, Cloud Computing, Data Engineering, Artificial Intelligence, Machine Learning, Cybersecurity, Information Systems, or a related discipline.
• A Masters degree in Computer Science, Information Technology, Software Engineering, Cloud Computing, Artificial Intelligence, Data Engineering, or a related field will be an added advantage.
• Professional certifications in cloud computing, infrastructure engineering, platform engineering, cybersecurity, DevOps, container platforms, or related disciplines will be an added advantage.
• Experience with Kubernetes, Docker, Terraform, Ansible, Git, cloud-native services, or equivalent platform engineering technologies will be an added advantage.
• Relevant cloud, infrastructure, container-platform or security certification is an advantage.
• Minimum of 3 years of experience of relevant infrastructure, platform or cloud engineering experience, including support for data or AI workloads.
• Skills in Linux, networking, containers, Python or shell scripting and automated infrastructure configuration.
• Experience with identity integration, secrets management, observability, backup and recovery.
• Ability to assess capacity, service dependencies, performance and operating costs across approved environments.
• Experience working with AI, machine learning, big data, analytics, or high-performance computing environments and supporting associated platform requirements.

• Translate approved AI workload requirements into platform designs, capacity estimates and implementation plans with ICT and architecture owners.
• Build and configure approved compute, storage, networking and platform services for development, testing and production environments.
• Create reusable, version-controlled infrastructure configurations and environment templates to reduce manual setup and configuration drift.
• Integrate identity, role-based access and service accounts with approved bank controls; separate user, administrator and workload permissions.
• Implement approved connectivity, encryption and secrets-management controls with ICT and Cybersecurity; retain configuration evidence.
• Configure shared tools for development, experiment tracking, artefact storage and model serving, with clear service ownership and support boundaries.
• Provide approved access to generative AI services with usage limits, logging and data-handling controls aligned to permitted business use.
• Assess platform compatibility, scalability and performance under expected workloads; document constraints and recommend capacity improvements.
• Build platform health metrics and alerts for resource use, availability and infrastructure faults; hand over operational monitoring to ML Ops.
• Test platform upgrades, patches and dependency changes in controlled environments before authorised release with ICT and ML Ops.
• Engineer and test backup, restore and resilience arrangements against approved service requirements with ICT and continuity owners.
• Resolve platform defects and supplier issues, supporting incident diagnosis while ML Engineers address solution code and Data Scientists address model behavior.
• Maintain platform architecture, configuration records, runbooks and handover evidence; support security review and independent assurance requests.
• Report platform capacity, reliability, provisioning time and cost; recommend improvements and retire resources through approved change processes.
• Implement and maintain automated infrastructure provisioning, configuration management, and platform deployment processes to improve consistency, efficiency, and scalability across AI environments.

• Linux
• networking
• containers
• Python
• shell scripting
• automated infrastructure configuration
• identity integration
• secrets management
• observability
• backup and recovery
• Kubernetes
• Docker
• Terraform
• Ansible
• Git
• cloud-native services

• Bachelors degree in Computer Science, Information Technology, Software Engineering, Cloud Computing, Data Engineering, Artificial Intelligence, Machine Learning, Cybersecurity, Information Systems, or a related discipline.
• A Masters degree in Computer Science, Information Technology, Software Engineering, Cloud Computing, Artificial Intelligence, Data Engineering, or a related field will be an added advantage.
• Professional certifications in cloud computing, infrastructure engineering, platform engineering, cybersecurity, DevOps, container platforms, or related disciplines will be an added advantage.
• Experience with Kubernetes, Docker, Terraform, Ansible, Git, cloud-native services, or equivalent platform engineering technologies will be an added advantage.
• Relevant cloud, infrastructure, container-platform or security certification is an advantage.