Senior AI Platform Engineer (Cloud) - KE

Absa Group's Chief Data Analytics and Applied AI Office (CDAIO) requires a technically exceptional and commercially grounded AI Platform Engineer (Cloud) to design, build, operate, and continuously optimise the multi-cloud AI infrastructure that powers the bank's enterprise AI capability. This role is the engineering backbone of a platform that supports various live AI projects across four business units (CIB, PPB, BB, and AR) and ten countries. The role demands deep technical mastery in cloud AI infrastructure, AI FinOps, zero-trust security architecture, agentic AI infrastructure, and platform observability, combined with the commercial fluency to govern AI compute costs at enterprise scale and communicate trade-offs to senior business and finance stakeholders.

• Lead the design, deployment, and continuous optimisation of Absa's multi-cloud AI platform stack: AWS Bedrock, Databricks AI, Microsoft Azure AI Foundry, Hugging Face Model Hub, and on-demand GPU clusters.
• Architect scalable, resilient, and reusable platform components including AI Gateway configuration, model serving infrastructure, vector database deployments, and data pipeline integration to support bank-wide AI delivery.
• Define and maintain infrastructure-as-code (IaC) standards e.g. using Terraform or Pulumi, enabling repeatable, auditable multi-cloud AI deployments across Absa's operating territories (10 countries).
• Lead the design and operation of agentic AI infrastructure: orchestration runtime environments e.g. Microsoft Foundry Agent Service, AWS Bedrock Agents, tool-calling schemas, agent memory and state management patterns, and multi-agent communication protocols.
• Develop and enforce cloud-agnostic model serving patterns to reduce platform lock-in and ensure workload portability across the CDAIO's multi-vendor stack.
• Identify and select appropriate internal and external technologies to deliver AI platform services; apply excellent judgement in continuously improving platform engineering practices.
• Take full accountability for end-to-end platform quality, completeness, and user experience across the development, deployment, and operational lifecycle.
• Positively contribute to the design and evolution of Group Architecture, infrastructure standards, and AI platform governance frameworks.
• Own the AI compute cost model for the CDAIO, including chargeback and showback frameworks for Databricks DBU consumption, AWS Bedrock token-based pricing, Azure AI Foundry provisioned throughput units, and GPU cluster utilisation across all four business units.
• Design and maintain FinOps dashboards and cost attribution reports using AWS Cost Explorer, Databricks System Tables cost analytics, and Azure OpenAI utilisation tooling — providing monthly cost-per-use-case reporting to Group Finance and the CDAIO COO.
• Evaluate and manage provisioned throughput versus on-demand consumption trade-offs for production AI workloads, presenting optimisation recommendations to the CDAIO and BU technology leads.
• Identify and execute AI compute cost optimisation opportunities: workload scheduling, spot instance strategies for training workloads, model distillation to reduce inference cost, and right-sizing of GPU clusters.
• Create business cases and solution specifications for AI platform investments and governance processes, including CTO and architecture approvals.
• Collaborate with the FinOps capability within the CDAIO COO to align AI platform costs to agreed budget envelopes and ensure spend anomalies are detected and escalated proactively.
• Define, implement, and own AI-specific SLAs and OLAs covering inference latency, platform availability, token throughput, API gateway response times, and model serving reliability, with explicit targets agreed with each business unit.

• Postgraduate degree in a quantitative discipline such as Computer Science, Data Science, Mathematics, Statistics, Engineering, or equivalent Masters-essential or PhD-advantageous.
• Certification in: Cloud - AWS Solutions Architect Professional, AWS Machine Learning Specialty, or Microsoft Azure AI Engineer Associate.
• FinOps - FinOps Foundation Certified Practitioner (FOCP) or equivalent AI cost governance credential.
• Security Certification - Certified Cloud Security Professional (CCSP) or AWS Security Specialty.
• IaC Certification - HashiCorp Terraform Associate or equivalent infrastructure-as-code credential.
• 5-8 years of progressive leadership experience in Cloud AI Platform Engineering, with production experience managing multi-cloud AI platform stacks across at least two of: AWS Bedrock/SageMaker, Databricks AI, Microsoft Azure AI Foundry, or Hugging Face enterprise deployments.
• 2–3-year experience in the following: AI FinOps and Cost Governance: Demonstrated ownership of AI compute cost models and FinOps reporting in a multi-BU or multi-cloud environment, with evidence of cost optimisation outcomes.
• AI Security Architecture: Designing and implementing zero-trust AI security (OAuth/OIDC, JWT, prompt injection controls, data residency compliance) in a regulated environment.
• Agentic AI Infrastructure: Production design of agent orchestration infrastructure such as LangGraph, AutoGen, Foundry Agent Service, Bedrock Agents, tool-calling APIs, and agent state management.
• Platform Observability: Operating AI-specific observability tooling for inference latency, drift alerting, and capacity management such as Prometheus, Grafana, Datadog, or Lakehouse Monitoring.
• Infrastructure-as-Code: Terraform, Pulumi, or equivalent for multi-cloud, multi-region AI infrastructure deployments; CI/CD pipeline design for platform components.
• Regulated Industry: AI platform engineering in financial services or a similarly regulated sector with model risk governance and change management obligations.