AI Platform Engineer (Cloud) - KE

Absa Group's Chief Data Analytics and Applied AI Office (CDAIO) requires an experienced and technically capable AI Platform Engineer (Cloud) to support the design, deployment, operation, and continuous improvement of the multi-cloud infrastructure powering the bank's enterprise AI capability. The role will contribute to the delivery of secure, scalable, reliable, and cost-effective AI platform services across multiple business units and countries. The platform supports AI use cases across Corporate and Investment Banking (CIB), Personal and Private Banking (PPB), Business Banking (BB), and Absa Regional Operations (AR). The successful candidate will work across technologies such as AWS Bedrock, Databricks AI, Microsoft Azure AI Foundry, Hugging Face, Kubernetes, and GPU-based infrastructure.

• Support the design, deployment, configuration, and operation of Absa's multi-cloud AI platform stack, including AWS Bedrock, Databricks AI, Microsoft Azure AI Foundry, Hugging Face, and GPU clusters.
• Build and maintain reusable platform components such as AI Gateway configurations, model serving environments, vector databases, API integrations, data pipelines, and containerised workloads.
• Develop and maintain infrastructure-as-code using technologies such as Terraform, Pulumi, AWS CDK, or equivalent tools.
• Contribute to repeatable and auditable infrastructure deployments across multiple cloud environments, regions, and operating countries.
• Configure and support agentic AI infrastructure, including orchestration environments, tool-calling APIs, agent memory, state management, and integration with enterprise systems.
• Implement cloud-agnostic model serving patterns that improve workload portability across AWS, Azure, Databricks, and Kubernetes-based environments.
• Support Kubernetes-based AI workloads using Docker, Kubernetes, and Helm.
• Assist with the evaluation and implementation of new platform technologies, services, and engineering patterns.
• Participate in architectural reviews, technical design sessions, peer reviews, and platform improvement initiatives.
• Create and maintain architectural diagrams, configuration documentation, operational procedures, and technical standards.
• Take accountability for the quality, performance, and operational readiness of assigned platform components.
• Escalate complex architectural, security, capacity, and operational risks to senior engineers and platform leadership.
• Monitor and analyse AI platform consumption across Databricks, AWS, Azure, GPU infrastructure, and third-party services.
• Support the development and maintenance of chargeback and showback frameworks for business units and individual AI use cases.
• Assist with cost attribution for Databricks DBU consumption, AWS Bedrock token usage, Azure AI Foundry provisioned throughput, and GPU workloads.
• Develop and maintain FinOps dashboards and cost reports using tools such as AWS Cost Explorer, Databricks System Tables, Azure Cost Management, and cloud-native monitoring services.
• Contribute to monthly cost-per-use-case reporting for Finance, platform leadership, and business unit stakeholders.
• Identify opportunities to optimise AI compute costs through workload scheduling, infrastructure right-sizing, token usage controls, caching, spot instances, and efficient model selection.
• Support assessments of provisioned throughput versus on-demand consumption for production AI workloads.
• Monitor spend anomalies and escalate unexpected usage, capacity, or budget risks.
• Provide technical input into business cases and investment proposals for AI platform services.
• Work closely with FinOps specialists and senior platform engineers to ensure infrastructure consumption remains within agreed budget parameters.
• Implement and maintain observability tooling for AI platform infrastructure.

• Bachelor's degree in Computer Science, Information Technology, Data Science, Mathematics, Statistics, Engineering, or a related quantitative discipline is essential. A postgraduate qualification is advantageous. Relevant practical experience may be considered where supported by a strong record of cloud and platform engineering delivery.
• One or more of the following certifications would be advantageous: Cloud AWS Certified Solutions Architect, AWS Certified Machine Learning Engineer, Microsoft Certified: Azure AI Engineer Associate, Microsoft Certified: Azure Solutions Architect Expert, Databricks Certified Data Engineer or Machine Learning certification, HashiCorp Certified: Terraform Associate, Equivalent Terraform, Pulumi, or cloud infrastructure certification, FinOps Certified Practitioner, Equivalent cloud cost management or financial operations certification, Certified Cloud Security Professional, AWS Certified Security, Microsoft Security, Compliance, and Identity certification, Equivalent cloud or cybersecurity certification.
• Approximately 4 to 6 years of relevant experience in cloud engineering, platform engineering, DevOps, MLOps, infrastructure engineering, or AI platform engineering.
• At least 2 years of practical experience supporting cloud-based data, machine learning, generative AI, or AI platform workloads in a production environment.
• Production experience with at least two of the following: AWS Bedrock or Amazon SageMaker, Databricks, Microsoft Azure AI Foundry or Azure Machine Learning, Hugging Face, Kubernetes-based model serving.
• Practical infrastructure-as-code experience using Terraform, Pulumi, AWS CDK, or an equivalent technology.
• Experience building or supporting CI/CD pipelines for cloud infrastructure, platform components, data services, or machine learning workloads.
• Experience with Docker, Kubernetes, Helm, APIs, identity integration, and cloud-native platform services.
• Experience implementing monitoring, dashboards, alerts, and operational support processes.