Principal Engineer, Platform

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 Firmus Technologies is seeking a Principal Engineer, Platform to own the platform Firmus engineers build and release on, in public cloud and in our own data centres. Self-service and security come first: through one golden path, an engineer should be able to create a service, get an environment, and ship to production securely with minimal support. Everything you design must work across sites and scale to hundreds of thousands of GPUs in gigawatt-scale AI factories. KEY RESPONSIBILITIES Architecture and Decisions Own the architecture of the engineering platform and its multi-site management plane, in public cloud and on premises. Set a target architecture two to three years ahead and plan the steps from today's system to it. Design the platform around a repeatable scaling unit. Capacity grows in identical units, a failure stays inside one unit or site, and each site meets a defined standard before it carries the platform. Decide how internal Kubernetes and hypervisor clusters are built, networked, upgraded, and recovered. Keep Kubernetes conformant and close to upstream releases, with upgrades that need no downtime. New sites come up from code and the site deployment kit, and recovery is tested regularly. Decide how services are packaged, released, and promoted with progressive rollout, and how a failed release is rolled back and contained across sites. Lead design reviews for significant changes across the teams, sign off architecture decision records, and settle technical disputes escalated by engineers. Decide at pace with incomplete information, write down the assumptions, and revisit a decision when the evidence changes. Build and Evolve Own engineer self-service with an API-first design. The CLI, automation, and any developer portal all use the same platform API. Treat every ticket to the platform team as a gap in self-service and close the most common ones first. Keep a service catalogue in code, with an owner for every service and scorecards that show each team where its services stand against the golden path, production-readiness, and security standards. Provide multi-cluster deployment for internal teams and the customer product: one pipeline to every region and data centre, the customer product isolated from internal workloads, and sites that keep running when the orchestrator is unreachable. Let AI agents work on the delivery path under the same controls as engineers, with their own identity, least privilege, sandboxing, and a person approving every production change. Reliability, security, and cost Set SLOs and error budgets for the platform and use the budget to decide when relia