Senior AI Infrastructure Engineer, Kubernetes

About This Opportunity

We are representing a confidential AI infrastructure organization operating in the Asia Pacific region. Our client is seeking a Senior AI Infrastructure Engineer with deep Kubernetes expertise to own the technical design and delivery of their GPU-accelerated platform. This is a hands-on principal-level individual contributor role based in Sydney, Australia.

You will be responsible for building production-grade cluster lifecycle, control-plane, networking, storage, security, observability, and automation capabilities across bare-metal environments. This role requires solving the hardest platform engineering problems and setting engineering standards for a secure, resilient, multi-tenant platform deployed at scale.

Responsibilities

• Define and own the Kubernetes platform reference architecture across management and workload clusters, including control-plane topology, cluster lifecycle, multi-tenancy, workload isolation, and failure-domain design
• Build and maintain backend services, APIs, controllers, operators, and automation required to provision, configure, upgrade, scale, and retire Kubernetes clusters reliably
• Engineer repeatable bare-metal Kubernetes deployment and lifecycle workflows using infrastructure-as-code and automated provisioning technologies such as Cluster API, kubeadm, Redfish, PXE, Ironic, or Metal3
• Design and operate cluster networking across CNI, ingress, service discovery, DNS, load balancing, network policy, and service mesh; integrate Multus, SR-IOV, BGP, InfiniBand, or RoCE for high-performance AI workloads
• Define persistent-storage and data-service patterns using CSI, Ceph, local NVMe, object storage, backup and restore, and disaster-recovery mechanisms for stateful platform and AI workloads
• Integrate and productionise NVIDIA GPU and Network Operators, device plugins, drivers, DCGM telemetry, scheduling, quotas, and topology-aware placement for multi-node accelerated workloads
• Establish GitOps and CI/CD patterns for platform software, configuration, policy, and release management with safe testing, progressive rollout, rollback, and upgrade practices
• Build platform security through identity and access control, RBAC, secrets management, policy-as-code, image and software-supply-chain controls, tenant isolation, and auditable change management
• Define service-level objectives and engineer observability for metrics, logs, traces, events, capacity, and performance; lead diagnosis of complex distributed systems failures
• Set engineering standards, design patterns, review practices, and operational readiness criteria; mentor senior engineers and resolve cross-team technical decisions

What We're Looking For

• 7+ years of progressive infrastructure, systems, or platform engineering experience, including substantial ownership of production Kubernetes platforms
• At least 3 years operating at senior staff, principal, or equivalent level
• Deep knowledge of Kubernetes internals, including API server, etcd, scheduler, controller manager, kubelet, admission, CRI, CNI, CSI, reconciliation patterns, cluster performance, upgrades, and control-plane failure modes
• Demonstrated experience designing, building, and operating highly available, large-scale, multi-cluster Kubernetes platforms on bare metal, private cloud, or hybrid infrastructure
• Strong software engineering ability in Go and/or Rust, with practical Python and Bash skills; experience building Kubernetes operators, controllers, admission webhooks, CLIs, or platform services
• Expert Linux systems knowledge, including namespaces, cgroups, systemd, kernel, host networking and container runtime behaviour, performance analysis, and low-level troubleshooting
• Strong Kubernetes networking expertise across Cilium, Calico, or equivalent CNI implementations, plus load balancing, DNS, ingress, BGP, network policy, and multi-network architectures
• Strong infrastructure automation and GitOps experience with tools such as Terraform, Ansible, Argo CD, Flux, GitHub Actions, GitLab CI, or Jenkins
• Practical experience with Kubernetes security and governance, including RBAC, OPA Gatekeeper or Kyverno, secrets management, certificate lifecycle, image security, and workload isolation
• Experience implementing production observability with Prometheus, Grafana, OpenTelemetry, Loki, Elasticsearch, or equivalent technologies
• Experience with GPU-enabled Kubernetes infrastructure, NVIDIA GPU Operator, accelerator scheduling for AI workloads at large scale, RDMA networking, and distributed AI workload requirements
• Experience with distributed storage and data services such as Ceph, CSI-backed storage, object storage, backup and restore, and disaster recovery
• CKA-level expertise expected; CKA, CKS, or relevant cloud-native certifications strongly preferred
• Bachelor's degree in computer science, engineering, or related discipline, or equivalent depth of practical engineering experience
• Clear technical judgement and communication, with a record of influencing architecture across software, networking, security, platform, and operations teams

How We Work With You

Candidates apply directly through Get A Job.ai. Our recruiting team will screen your application and conduct an initial interview. If there is a strong match, we will submit your profile to our client for consideration. Please do not contact the client directly, as all communication is managed through our talent team to ensure a professional and confidential process.

Pay

Compensation details will be discussed during the screening process based on experience and qualifications.

Equal Opportunity: Get A Job.ai is committed to inclusive hiring practices. We welcome applications from candidates of all backgrounds and work with clients who share our commitment to building diverse teams.