Job Summary

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. The role requires practical experience in cloud platform engineering, infrastructure-as-code, AI workload deployment, platform observability, cloud cost optimisation, security controls, and agentic AI infrastructure.

The role includes applying critical thinking, design thinking, and problem-solving skills within an agile engineering environment to address complex platform challenges. The AI Platform Engineer will work closely with senior engineers, architects, security teams, FinOps specialists, and AI Solution Engineers to deliver high-quality platform services in line with Absa’s architecture, risk, security, and responsible AI requirements.

The successful candidate will take accountability for assigned platform components and services while contributing to the broader performance, resilience, and user experience of the enterprise AI platform.

Key Focus Areas

• AI Platform Engineering and Architecture – Support the design, deployment, and operation of enterprise-grade, multi-cloud AI infrastructure across AWS Bedrock, Databricks AI, Microsoft Azure AI Foundry, Hugging Face, and GPU environments.
• AI FinOps and Compute Cost Optimisation – Monitor AI infrastructure consumption, support cost allocation and reporting, and identify opportunities to optimise token usage, Databricks consumption, provisioned throughput, and GPU utilisation.
• Platform Observability and Reliability – Implement and maintain monitoring, alerting, dashboards, and operational processes to ensure the availability, performance, and reliability of production AI platform services.
• AI Security and Zero-Trust Controls – Implement security controls for AI platform APIs, model endpoints, data pipelines, and agentic AI services in line with Absa’s security architecture and regulatory requirements.
• Agentic AI Infrastructure – Support the deployment and operation of infrastructure enabling AI agents, tool-calling services, autonomous workflows, agent memory, and orchestration frameworks.
• Agile Engineering and Collaboration – Deliver platform enhancements through agile practices while collaborating with engineers, architects, business units, security teams, risk stakeholders, and third-party technology providers.

• 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.

• Cloud platform engineering
• Infrastructure-as-code
• AI workload deployment
• Platform observability
• Cloud cost optimisation
• Security controls
• Agentic AI infrastructure
• AWS Bedrock
• Databricks AI
• Microsoft Azure AI Foundry
• Hugging Face
• Kubernetes
• GPU-based infrastructure
• Terraform
• Pulumi
• AWS CDK
• Docker
• Helm

• Practical experience in cloud platform engineering, infrastructure-as-code, AI workload deployment, platform observability, cloud cost optimisation, security controls, and agentic AI infrastructure.
• Experience working across technologies such as AWS Bedrock, Databricks AI, Microsoft Azure AI Foundry, Hugging Face, Kubernetes, and GPU-based infrastructure.
• Ability to apply critical thinking, design thinking, and problem-solving skills within an agile engineering environment.