Platform Engineer, AI

About Us
SUSE is a global leader of enterprise open source software. By transforming community innovations into secure, sovereign and AI-ready solutions, SUSE empowers customers to escape vendor lock-in and regain control of their IT destiny. Through industry-leading Linux, Kubernetes, Edge and AI infrastructure solutions, SUSE delivers the flexibility to innovate everywhere—from the data center to multi-cloud and out to the edge. Only SUSE also manages many Linux and Kubernetes distributions. At SUSE, Choice Happens because we prioritize community, interoperability and relentless innovation. Discover how we power mission-critical .
Platform Engineer, AI
Job Description
About the Role
SUSE Internal IT is hiring a Platform Engineer to join the Shared Services team. Your primary focus is building and operating our internal Agentic AI Platform, working as one of a pair of engineers who share ownership of it end-to-end, but the role also flexes into the team’s wider Shared Services portfolio (source control, CI/CD, monitoring, backup, and the other systems the team runs) as priorities require, including shared operational rotations. This role reports to the Senior Platform Engineering Manager, Shared Services.
This is a hands-on engineering role. You will be equally responsible for building new capabilities, keeping systems operationally healthy, and maintaining the infrastructure-as-code and documentation that underpins them: both on the Agentic AI Platform and, when called on, elsewhere in Shared Services. On the platform specifically, you’ll pair with one other engineer: sharing ownership, peer-reviewing each other’s work, and developing complementary depth over time.
The platform is in active delivery. You will join at a point where the core infrastructure is running and the next phase of security hardening, automation, and observability is under way. There is meaningful work to deliver from day one.
Key Responsibilities
You’ll build out the platform’s foundational layers: security and secrets management, policy enforcement, observability, delivery automation, workload scaling, and AI traffic routing. You will also translate architectural designs into production-grade infrastructure-as-code, rather than working against a fixed feature list. Day to day, you own the platform’s operational health: monitoring, incident response, root-cause analysis, and remediation, including standing up and operating local LLM inference capability (e.g. vLLM on GPU nodes) so internal workloads can run inference on-prem. You’ll manage secrets rotation, certificate lifecycle, and identity configuration as ongoing responsibilities, and participate in planned high-stakes procedures such as secrets infrastructure initialisation and rotation events. Beyond the platform itself, you’ll contribute engineering and review capacity to Shared Services more broadly, and take part in the team’s shared operational and on-call rotations. You’re expected to keep your infrastructure-as-code versioned and peer-reviewed, proactively chase down technical debt, maintain runbooks and documentation so any team member can operate what you’ve built, and peer-review your platform counterpart’s changes in turn.
Required Skills
Candidates will need to demonstrate hands-on production delivery experience, not just conceptual familiarity. We expect evidence of real delivery against each of these areas at the interview.

• Kubernetes – production cluster operation (RKE2, EKS, GKE, or equivalent); Helm, RBAC design, multi-namespace workload management
• Secrets management — production deployment of a secrets management platform (HashiCorp Vault or equivalent), covering PKI, dynamic credentials, and workload secrets injection
• Policy-as-code – admission control policy authoring and enforcement in production Kubernetes environments (OPA/Rego, Kyverno, or equivalent)
• GitOps – Fleet, ArgoCD, Flux, or equivalent at production scale; declarative drift reconciliation, rollback strategy, multi-environment targeting
• Observability stack – log aggregation, log pipeline design, distributed tracing (OpenTelemetry or equivalent), and metrics dashboards (Prometheus/Grafana or equivalent)
• API/AI gateway and model serving – production deployment of an API or AI gateway (Kong, Envoy, or equivalent) and local LLM inference serving (vLLM or equivalent), including GPU-aware scheduling
• Linux platform engineering – networking fundamentals, TLS and PKI, CSI storage operations, container runtime
  

• If this role is filled in Italy, the expected Total Target Compensation (“TTC”) range is between 58,000 EUR and 78,000 EUR gross annually. The TTC includes both the annual base salary and target corporate bonus opportunity, which is typically paid quarterly, as well as access