Senior Platform Security Engineer
- Market p25–p75
- AUD 125k–AUD 168k
- Opened · 30d
- +0
- Closed · 30d
- −0
- All security
- 8,270open
AI FactoryOS Operations
AI FactoryOS is Firmus' proprietary operating system for the AI Factory. It governs GPU telemetry, cooling, power and grid interaction as one integrated layer, so that every Firmus site can be optimised and monitored as a single system.
AI FactoryOS Operations runs that platform in production and owns the 24/7 reliability of AI FactoryOS, Firmus AI Cloud and the platforms built on them, together with the service levels the estate is measured against.
The remit is an engineering one. The function builds the guarded automation, remediation and operational tooling that turn manual response into a software-defined capability, and builds and operates the shared services the estate's own operation depends on. The function works closely with the engineering teams that build the platform, supplying the production evidence that shapes what they fix and what they build next.
Senior Platform Security Engineer
Role Summary
Firmus runs large-scale, state-of-the-art AI infrastructure built on the latest generation of GPU rack-scale systems and operated as one estate to power the next generation of AI innovation. The Senior Platform Security Engineer owns the operational security of that platform. The multi-tenant Kubernetes estate sits at its heart: production access, admission policy, workload identity and network policy in the live estate, and the operational security controls that keep it defensible as it grows.
This is a hands-on senior role with deep technical expertise. This role owns security in production: the technical response to security incidents on the function's 24/7 security on-call roster, runtime visibility and enforcement built with modern eBPF-based tooling, the security acceptance requirements a release has to meet before it is authorised, the authority to grant or refuse a security exception, and the deepest technical escalation for security-related platform faults.
Key Responsibilities
- Lead the technical response to security incidents across the estate as part of the 24/7 security on-call roster: triage alerts and reports escalated from first response, distinguish a security signal from an ordinary operational symptom, contain the threat, remove the cause and recover affected services, working under the incident commander, who holds the incident record, communications and customer commitment.
- Preserve and handle evidence during security incidents, including logs, host and container captures and access records, with a recorded chain of custody, so that findings stand up to customer, legal and regulatory scrutiny, and provide the technical input to any customer, contractual or regulatory notification made by the accountable leaders.
- Own the security incident playbooks for the estate's main threat scenarios (for example credential or key compromise, a breach of tenant isolation, a malicious or compromised workload, and software supply-chain compromise), keep them executable by any engineer on the roster, and test them through regular exercises with the incident commanders and the wider function.
- Build runtime visibility and enforcement using eBPF-based tooling (for example Cilium, Falco or Tetragon), feeding high-quality signal into the estate's security monitoring, tune detection so that alerts stay actionable as the estate grows, and turn repeat findings into guarded automated response where it is safe to do so.
- Own the security gates in the delivery pipeline: image signing, software bills of materials, vulnerability gating and policy checks, and maintain the policy-as-code controls that govern what ships to production, running on the delivery toolchain operated by Shared Services Operations.
- Set and own the security content of the multi-tenant Kubernetes estate: admission policy, workload identity and network policy, and verify that what runs in production matches it.
- Set the operational security standard the platform's Kubernetes control planes and baselines must meet in production, validate against it continuously, and raise deviations as engineering requirements to the AI Infrastructure team.