Job Description
Firmus Technologies
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Firmus Technologies is a global leader pioneering the development and operation of efficient AI infrastructure across Asia Pacific.
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Founded in Australia in ****, our mission is to create the most efficient AI infrastructure by combining cutting-edge technology with a steadfast commitment to sustainability.
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At Firmus, we are unique in our approach. We design, build, and operate 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.
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Firmus AI Cloud
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Our large-scale GPU cloud platform, Firmus AI Cloud, is purpose-built to deliver energy-efficient AI compute at scale to customers.
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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.
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Role Summary
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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.
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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.
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Key Responsibilities
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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.
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Build and maintain the backend services, APIs, controllers, operators, and automation required to provision, configure, upgrade, scale, and retire Kubernetes clusters reliably.
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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.
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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.
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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.
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Integrate and productionise NVIDIA GPU and Network Operators, device plugins, drivers, DCGM telemetry, scheduling, quotas, and topology‐aware placement for multi‐node accelerated workloads.
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Establish GitOps and CI/CD patterns for platform software, configuration, policy, and release management, with safe testing, progressive rollout, rollback, and upgrade practices.
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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.
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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.
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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.
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Skills & Experience
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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.
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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.
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Demonstrated experience designing, building,



and operating highly available, multi‐cluster Kubernetes platforms on bare metal, private cloud, or hybrid infrastructure.
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Solid software engineering ability in Go, with practical Python and Bash skills; experience building Kubernetes operators, controllers, admission webhooks, CLIs, or platform services.
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Expert Linux systems knowledge, including namespaces, cgroups, systemd, kernel and container runtime behaviour, performance analysis, and low‐level troubleshooting.
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Strong Kubernetes networking expertise across Cilium, Calico, or equivalent CNI implementations, plus load balancing, DNS, ingress, BGP, network policy, and multi‐network architectures.
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Strong infrastructure automation and GitOps experience with tools such as Terraform, Ansible, Argo CD, Flux, GitHub Actions, GitLab CI, or Jenkins.
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Practical experience with Kubernetes security and governance, including RBAC, OPA Gatekeeper or Kyverno, secrets management, certificate lifecycle, image security, and workload isolation.
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Experience implementing production observability with Prometheus, Grafana, OpenTelemetry, Loki, Elasticsearch, or equivalent technologies, and using telemetry to manage reliability, capacity, and performance.
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Experience with GPU‐enabled Kubernetes infrastructure, NVIDIA GPU Operator, accelerator scheduling, RDMA networking, and distributed AI workload requirements.
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Experience with distributed storage and data services such as Ceph, CSI‐backed storage, object storage, backup and restore, and disaster recovery.
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CKA‐level expertise is expected; CKA, CKS, or relevant cloud‐native certifications are strongly preferred.
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Bachelor's degree in computer science, engineering, or a related discipline, or equivalent depth of practical engineering experience.
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Clear technical judgement and communication, with a record of influencing architecture across software, networking, security, platform, and operations teams.
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Location & Reporting
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Australia (Sydney, NSW or Launceston, TAS)
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Reporting to Head of AI Platform
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Employment Basis
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Full‐time
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Diversity
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At Firmus, 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.
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Join us in our mission to revolutionize the AI industry through sustainable practices and cutting‐edge engineering.

📌 Principal Ai Infrastructure Engineer, Kubernetes (New South Wales)
🏢 Firmus Technologies
📍 New South Wales