AI Platform Engineers (AI/GenAI | Cloud | Kubernetes) - Contract - Onsite - Sandton

SUMMARY:
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POSITION INFO:
ENVIRONMENT: Our client is seeking highly specialised AI Platform Engineers to design, build, operate and optimise enterprise-grade AI infrastructure within a complex, regulated environment. This is not a general cloud engineering, IT infrastructure or data science role.   The successful candidates must have hands-on experience supporting production AI workloads across multi-cloud environments and must be comfortable working across AI infrastructure, model serving, agentic AI, security, observability, infrastructure-as-code and AI cost governance.   DUTIES: ·         Design, deploy and optimise scalable multi-cloud AI platform infrastructure. ·         Build reusable platform components for AI gateways, model serving, vector databases, data pipelines and GPU workloads. ·         Develop and maintain infrastructure-as-code using Terraform, Pulumi, CloudFormation or equivalent technologies. ·         Design infrastructure supporting agentic AI, including orchestration environments, tool-calling, agent memory, state management and multi-agent communication. ·         Implement cloud-agnostic model-serving patterns that support workload portability. ·         Define and manage AI platform SLAs covering availability, inference latency, throughput and reliability. ·         Implement platform observability, monitoring, incident management, release management and operational runbooks. ·         Design and implement zero-trust security controls for AI platforms. ·         Manage AI compute expenditure through cost attribution, chargeback\/showback, workload optimisation and usage reporting. ·         Maintain technical documentation, architectural decision records and governance evidence. ·         Mentor engineers and contribute to platform engineering standards and delivery practices.   REQUIREMENTS: •       Senior level: approximately 5–8 years of relevant cloud and AI platform engineering experience. •       Lead\/Principal level: approximately 8–12 years of relevant experience, including technical leadership and responsibility for engineering teams or platform squads.   Mandatory Technical Experience: •       Candidates must demonstrate meaningful production experience in most of the following: •       At least two of the following AI ecosystems: o    AWS Bedrock or SageMaker o    Microsoft Azure AI Foundry or Azure OpenAI o    Databricks AI o    Enterprise Hugging Face deployments •       Kubernetes, Docker, Helm and containerised platform services. •       Terraform, Pulumi, AWS CDK, CloudFormation or equivalent infrastructure-as-code. •       CI\/CD and automated deployment of cloud or AI platform components. •       Production model-serving infrastructure, AI gateways or inference endpoints. •       Platform observability using tools such as Prometheus, Grafana, Datadog, OpenTelemetry or Databricks Lakehouse Monitoring. •       Cloud security, identity and access management, including OAuth\/OIDC, JWT, RBAC or ABAC. •       Production incident management, SLAs, release management and operational readiness. •       Experience within banking, financial services or another highly regulated enterprise environment.   Specialist AI Experience: •       Candidates should demonstrate practical experience in one or more of the following: •       Agent orchestration frameworks such as LangGraph, AutoGen, AWS Bedrock Agents or Microsoft Foundry Agent Service. •       Model Context Protocol, tool-calling APIs and agent state or memory management. •       Retrieval-augmented generation and vector database infrastructure. •       Cloud-agnostic model serving using tools such as ONNX, BentoML, Triton Inference Server or vLLM. •       MLOps platforms such as MLflow, Kubeflow or Airflow. •       GPU cluster management and inference or training workload optimisation. •       Prompt-injection prevention, output filtering, data-exfiltration controls and AI threat modelling. •         AI Finops Experience: •       Candidates should have experience with some combination of: •       AI or cloud cost attribution and tagging. •       Chargeback and showback models. •       Token, GPU, DBU or provisioned-throughput cost management. •       Rightsizing, workload scheduling and reserved or spot-instance optimisation. •       Cost dashboards, anomaly detection and cost-per-use-case reporting. •       Communicating technical cost trade-offs to senior technology, business or finance stakeholders.   Qualifications: •       Postgraduate qualification in Computer Science, Information Technology, Data Science, Mathematics, Statistics, Engineering or a related quantitative field. •       A Master’s degree is preferred and may be required for certain senior appointments. •       Relevant certifications are strongly preferred, including: o    AWS Solutions Architect Professional or AWS Machine Learning o    Microsoft Azure AI Engineer o    FinOps Certified Practitioner o    Certified Cloud Security Professional or equivalent o    HashiCorp Terraform Associate Kubernetes certification