Firmus Technologies

Firmus 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 Firmus, 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.

Firmus AI Cloud

Our large-scale GPU cloud platform, Firmus 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

The Principal Kubernetes Engineer, AI Infrastructure owns the technical design and delivery of the backend infrastructure that powers the Firmus 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

- 10+ 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, upgrades, and control-plane failure modes.
- Demonstrated experience designing, building, and operating highly available, multi-cluster Kubernetes platforms on bare metal, private cloud, or hybrid infrastructure.
- Solid software engineering ability in Go, with

📌 Principal AI Infrastructure Engineer, Kubernetes (Sydney)
🏢 Firmus Technologies
📍 Sydney