Senior AI Infrastructure Engineer, Kubernetes

Engg; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Firmus Technologies in San Francisco seeks a Senior Kubernetes Engineer to design and deliver a secure, multi‑tenant AI infrastructure platform for GPU‑accelerated workloads.

You will own cluster lifecycle, control‑plane, networking, storage, and observability, collaborating across AI Platforms and Security teams while mentoring peers.

Qualifications

• 7+ years in infrastructure or platform engineering with production Kubernetes experience.
• In-depth Kubernetes internals knowledge, including API server, etcd, scheduler, and CSI.
• Experience designing and operating large multi-cluster Kubernetes platforms on bare metal or private cloud.
• Proficient in Go and/or Rust; practical Python/Bash skills; building Kubernetes operators or controllers.
• Strong Linux expertise and advanced networking, including CNI, BGP, and multi-network design.

Responsibilities

• Define and own the Kubernetes platform reference architecture for management and workload clusters.
• Build and maintain backend services, controllers, and automation to manage clusters lifecycle.
• Engineer bare‑metal Kubernetes deployment workflows using IaaC and provisioning tech (Cluster API, kubeadm).
• Design and operate cluster networking, including CNI, ingress, DNS, load balancers, and service mesh.
• Define storage patterns and disaster recovery for stateful AI workloads; ensure multi‑node GPU support.
• Establish GitOps and CI/CD patterns; enforce security, testing, rollout, and upgrade practices.

Skills

Kubernetes internals

Bare metal clusters

Go/Rust

Python Bash

Linux systems

Networking (CNI/BGP)

GitOps

Security & governance

Observability

GPU‑enabled infra

Tools

Cluster API

kubeadm

Redfish

PXE

Ironic

Metal3

Terraform

Argo CD

Flux

Job description

Firmus Technologies Firmus Technologies is a global leader 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 combining cutting‑edge technology with a steadfast commitment to sustainability. 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. Firmus AI Cloud Our large‑scale GPU cloud platform, Firmus AI Cloud, is purpose‑built to deliver energy‑efficient AI compute at 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 Senior 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

• 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 fo