Senior AI Platform Engineer (Cloud)

Absa is seeking a highly skilled Senior AI Platform Engineer (Cloud) to join its Chief Data Analytics and Applied AI Office (CDAIO). This critical role involves the design, implementation, and ongoing optimisation of the bank's multi-cloud AI infrastructure. The successful candidate will be instrumental in empowering Absa's enterprise-wide artificial intelligence capabilities, ensuring they align with the bank's strategic and commercial objectives. This position forms the technical foundation for a platform supporting numerous live AI projects across various business units and multiple countries.

About the Role

As the engineering backbone of Absa's AI platform, this role demands a unique blend of deep technical expertise and commercial acumen. You will be responsible for an infrastructure that enables the CDAIO to effectively steward the bank's AI capabilities. This includes overseeing the end-to-end delivery of the AI platform, ensuring robust governance, and promoting responsible AI use. The scope of your work will span across Absa's CIB, PPB, BB, and AR business units, impacting operations in ten different countries.

Your responsibilities will encompass critical areas such as designing and operating enterprise-grade, multi-cloud AI platform infrastructure, managing AI FinOps and compute cost governance, establishing platform observability and Service Level Agreements (SLAs), and architecting AI security with a zero-trust approach. Furthermore, you will play a key role in building the infrastructure necessary for agentic AI systems. This is an opportunity to apply critical thinking, design thinking, and problem-solving skills within an agile team, delivering high-quality, cost-effective solutions that adhere strictly to Absa's risk management framework, architecture standards, and AI Responsible Use Policy. You will own the quality, completeness, and user experience of the platform services end-to-end, while also contributing to the development of these capabilities in your colleagues.

The Senior AI Platform Engineer (Cloud) will be accountable for several key focus areas:

• **AI Platform Engineering and Architecture:**
• Lead the design, deployment, and continuous enhancement of Absa's multi-cloud AI platform stack, which includes technologies like AWS Bedrock, Databricks AI, Microsoft Azure AI Foundry, Hugging Face, and GPU clusters.
• Architect robust, scalable, and reusable platform components. This includes configuring AI Gateways, model serving infrastructure, deploying vector databases, and integrating data pipelines to support AI delivery across the bank.
• Define and uphold infrastructure-as-code (IaC) standards, utilising tools such as Terraform or Pulumi, to ensure consistent, auditable, and repeatable multi-cloud AI deployments across Absa's diverse operational territories.
• Spearhead the design and operation of agentic AI infrastructure, encompassing orchestration runtime environments, tool-calling schemas, agent memory and state management, and multi-agent communication protocols.
• Develop and enforce cloud-agnostic model serving patterns to minimise platform dependency and ensure workload portability across the CDAIO's multi-vendor ecosystem.
• Evaluate and select appropriate internal and external technologies to deliver AI platform services, demonstrating sound judgment in continuously refining platform engineering practices.

• **AI FinOps and Compute Cost Governance:**
• Take ownership of the AI compute cost model for the CDAIO, including the implementation of chargeback and showback frameworks for various consumption models (e.g., Databricks DBU, AWS Bedrock tokens, Azure AI Foundry throughput, GPU clusters).
• Design and maintain FinOps dashboards and cost attribution reports using relevant tools (AWS Cost Explorer, Databricks System Tables, Azure OpenAI utilisation tools) to provide monthly cost-per-use-case reporting to Group Finance and the CDAIO COO.
• Analyse and manage the trade-offs between provisioned throughput and on-demand consumption for production AI workloads, presenting optimisation recommendations to senior leadership.
• Identify and implement strategies for AI compute cost optimisation, such as workload scheduling, leveraging spot instances for training, model distillation for inference cost reduction, and right-sizing GPU clusters.
• Develop business cases and solution specifications for AI platform investments and governance processes, securing necessary CTO and architecture approvals.
• Collaborate closely with the FinOps capability within the CDAIO COO to align AI platform costs with budgetary expectations and manage any spending anomalies.

• **Platform Observability and AI Security:**
• Establish AI-specific service reliability standards, implement comprehensive observability tooling, and define incident management processes for production AI workloads that support numerous live projects.
• Design and implement zero-trust security principles, integrate OAuth/OIDC, and develop prompt injection controls to protect Absa's AI platform, ensuring compliance with data residency regulations across different jurisdictions.

This role is ideal for an experienced engineer who thrives on solving complex challenges in a dynamic environment and is passionate about building scalable, secure, and cost-effective AI infrastructure.

In South Africa, senior engineering roles within financial services, particularly those focused on advanced technologies like AI and cloud infrastructure, are highly sought after. While specific remuneration varies greatly, such positions typically command competitive salaries reflecting the specialised skills and significant responsibility involved. Absa, as a major financial institution, often offers structured career development paths, opportunities for continuous learning, and the chance to work on transformative projects that have a substantial impact across the African continent.

QUALIFICATIONS AND EXPERIENCE 

 Education/ Qualification: 

• 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. 

 Work Experience: 

• 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.  

• Regulated Industry: AI platform engineering in financial services or a similarly regulated sector with model risk governance and change management obligations 

• Advantageous: 

• People leadership: Leading or mentoring a team of platform or infrastructure engineers in an agile delivery environment. 

• Pan-African Deployments: Delivering AI platform services across multiple African jurisdictions with awareness of data localisation and cross-border data transfer requirements. 

Knowledge and Skills:

• Multi-Cloud AI Platform Architecture: Expert design and operation of AWS Bedrock, Databricks AI, Azure AI Foundry, and Hugging Face in enterprise production environments across multiple business units and geographies.

• Agentic AI Infrastructure: Practical production knowledge of agent orchestration frameworks (LangGraph, AutoGen, Foundry Agent Service, Bedrock Agents), tool-calling API design, agent memory architecture, and multi-agent coordination patterns.

• AI FinOps and Cost Management: Chargeback and showback model design; DBU and token cost attribution; provisioned throughput versus on-demand optimisation; GPU cluster cost management; spend anomaly detection and FinOps dashboarding.

• AI Security and Zero Trust: OAuth 2.0, OIDC, JWT/JWE/JWS; RBAC and ABAC for AI workloads; prompt injection prevention; data exfiltration controls at the Gateway layer; AI threat modelling and data residency compliance.

• Infrastructure-as-Code: Terraform, Pulumi, or AWS CDK for multi-cloud AI infrastructure; CI/CD pipeline design for platform components; container orchestration using Docker, Kubernetes, and Helm.

• Platform Observability: Prometheus, Grafana, Datadog, OpenTelemetry, and Databricks Lakehouse Monitoring; custom metric design for AI workload health including inference latency, token throughput, and model drift.

• Cloud-Agnostic Model Serving: ONNX, BentoML, Triton Inference Server; containerised model deployment patterns for portability across AWS, Azure, and Databricks environments.

• MLOps Tooling: Working knowledge of MLflow, Kubeflow, Airflow, and CI/CD for ML, sufficient to collaborate effectively with AI Solution Engineers on model deployment and lifecycle management

• GPU and HPC Architecture: On-demand GPU cluster management; spot instance strategies; high-performance compute cost optimisation for large-scale model training and fine-tuning workloads.

• Enterprise Risk and Governance: Absa Enterprise Wide Risk Management Framework; Group Architecture standards; AI Responsible Use Policy; POPIA; country-specific data localisation requirements across Absa's ten operating countries.

• Agile Delivery: Sprint planning, backlog management, and continuous delivery practices in a self-directed squad environment; experience removing delivery barriers in a fast-moving, multi-stakeholder context.

Education

Bachelor's Degree: Information Technology

Absa Bank Limited is an equal opportunity, affirmative action employer. In compliance with the Employment Equity Act 55 of 1998, preference will be given to suitable candidates from designated groups whose appointments will contribute towards achievement of equitable demographic representation of our workforce profile and add to the diversity of the Bank.

Absa Bank Limited reserves the right not to make an appointment to the post as advertised