Senior AI Infrastructure Engineer, Kubernetes

Firmus; Sydney; On-site; AUD 180,000 - 240,000; Full time

Benefits offered by this job

None

Job summary

the company Technologies is seeking a Senior Kubernetes Engineer to own the backend infrastructure powering the company Kubernetes platform. This hands-on principal role focuses on building production-grade cluster lifecycle, control-plane, networking, storage, security, observability, and automation for GPU-accelerated environments.

You will define standards, drive GitOps adoption, and collaborate across AI Platforms, Solutions Architecture & Delivery, networking, security, and operations to

Qualifications

• 7+ years of progressive infrastructure, systems, or platform engineering experience, including ownership of production Kubernetes platforms.
• Deep knowledge of Kubernetes internals (API server, etcd, scheduler, controller manager, kubelet).
• Experience designing and operating large-scale Kubernetes platforms on bare metal, private cloud, or hybrid.
• Strong software engineering ability in Go and/or Rust, with Python and Bash skills; experience building operators, controllers, or platform services.
• Expert Linux systems knowledge and strong networking and observability skills.
• Experience with GPU-enabled Kubernetes infrastructure and distributed AI workloads.
• CKA-level expertise; cloud-native certifications preferred.
• Bachelor’s degree in computer science, engineering, or related discipline.

Responsibilities

• Define and own the Kubernetes platform reference architecture across management and workload clusters.
• Build and maintain backend services, APIs, controllers, operators, and automation to manage Kubernetes clusters.
• Engineer repeatable bare-metal Kubernetes deployment and lifecycle workflows using IaC and provisioning technologies.
• Design and operate cluster networking, service discovery, and load balancing; integrate high-performance networking where required.
• Define persistent-storage patterns using CSI, Ceph, local NVMe, and disaster-recovery mechanisms.
• Integrate NVIDIA GPU and Network Operators, device plugins, and topology-aware placement.
• Establish GitOps and CI/CD patterns for platform software and release management.
• Define security controls, RBAC, secrets, policy-as-code, and audit capabilities.
• Define observability for metrics, logs, traces, and capacity; diagnose complex distributed failures.
• Set engineering standards and mentor engineers while contributing to implementation.

Job description

the company Technologies

the company Technologies is a globalleader pioneering the development and operation of efficient AI infrastructure across Asia Pacific.

Founded in Australia in 2019, our mission is to create the most efficient AI infrastructure by combiningcutting-edgetechnology with a steadfast commitment to sustainability.

At the company, we are unique in our approach. We design, build, andoperate a new class of digital infrastructure – the AI Factory. Through our model-to-grid technology approach, we have pushed the boundaries of multi-generational liquid cooling systems, energy management, AI software orchestration, and construction. For our customers, this approach allows us to make every watt count and deliver low-cost AI tokens globally.

the company AI Cloud

Our large-scale GPU cloud platform, the company AI Cloud, is purpose-built to deliver energy-efficient AIcomputeat scale to customers.

It empowers developers, enterprises, educational institutions, and government users to train and deploy AI models with unmatched efficiency and cost savings. With an ever-growing suite of services and applications, we are committed to delivering a cloud experience that is market-leading, proprietary, and built to scale.

Role Summary (Location Melbourne or Sydney)

The Senior Kubernetes Engineer, AI Infrastructure owns the technical design and delivery of the backend infrastructure that powers the the company Kubernetes platform. This is a hands-on principal-level individual contributor role, responsible for building production-grade cluster lifecycle, control-plane, networking, storage, security, observability, and automation capabilities across GPU-accelerated bare-metal environments.

They solve the hardest platform engineering problems, set Kubernetes engineering standards, and provide domain-level technical sign-off for platform designs. They work across AI Platforms, Solutions Architecture & Delivery, networking, security, and operations to create a secure, resilient, multi-tenant platform that can be deployed and operated consistently at AI-factory scale.

Key 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 the 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 where required 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 appropriate 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 into the architecture 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 and eliminate recurring operational toil.

• Set engineering standards, design patterns, review practices, and operational readiness criteria; mentor senior engineers and resolve cross-team technical decisions while remaining directly involved in implementation.

Skills & Experience

• 7+ years of progressive infrastructure, systems, or platform engineering experience, including substantial ownership of production Kubernetes platforms and at least 3 years operating at senior staff, principal, or equivalent level.

• Deep knowledge of Kubernetes internals, including the 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 and 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, and using telemetry to manage reliability, capacity, and performance.

• 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 is expected; CKA, CKS, or relevant cloud-native certifications are strongly preferred.

• Bachelor’s degree in computer science, engineering, or a 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.

Location & Reporting

• Australia (Sydney, NSW or Launceston, TAS)

• Reporting to Head of AI Platform

Employment Basis

Full-time

Diversity

At the company, we are committed to building a diverse and inclusive workplace. We encourage applications from candidates of all backgrounds who are passionate about creating a more sustainable future through innovative engineering solutions.