Platform Engineer, Ai

About the Role SUSE Internal IT is hiring Platform Engineers to join the team building and operating our internal Agentic AI Platform. This is a hands‑on engineering role. You will be equally responsible for building new platform capabilities, keeping the platform operationally healthy, and maintaining the infrastructure‑as‑code and documentation that underpins it. You will work with one other engineer as a pair: sharing ownership of the full platform, peer‑reviewing each other’s work, and developing complementary depth across the stack 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 underway. There is meaningful work to deliver from day one.

Key Responsibilities

Implement new platform capabilities from architectural designs, translating security, governance, and infrastructure requirements into production‑grade infrastructure‑as‑code

Design and build the platform security and secrets management layer, ensuring all workloads operate with least‑privilege credentials and certificates issued through a governed PKI hierarchy

Implement and enforce security policy across the cluster using admission control, covering workload configuration, image standards, network traffic, and resource constraints

Build and establish the platform observability stack, providing consistent log aggregation, metrics, distributed tracing, and alerting across all platform components

Design and implement GitOps delivery automation, ensuring all platform changes flow through version‑controlled, auditable pipelines with drift reconciliation

Build and configure workload autoscaling, ensuring AI workflow workers scale efficiently and cost‑effectively in response to demand

Implement the AI model routing and gateway layer, enabling governed, auditable routing of model traffic with per‑consumer rate limiting

Operate

Own the day‑to‑day operational health of the platform: monitor for issues, respond to incidents, conduct root‑cause analysis, and implement lasting remediation

Maintain the health of platform data services — database cluster, job queue, and object storage — including backup schedules, failover testing, and capacity management

Monitor and tune autoscaling and resource configuration as workload patterns evolve, ensuring the platform scales responsively without over‑provisioning

Manage secrets rotation, certificate lifecycle, policy drift detection, and identity configuration as ongoing operational responsibilities

Participate in planned high‑stakes operational procedures — such as secrets infrastructure initialisation and rotation events — applying disciplined, documented execution

Maintain

Own and evolve the infrastructure‑as‑code for your areas of the platform; keep all configurations versioned, peer‑reviewed, and aligned with the architectural design

Proactively identify and resolve technical debt — manual processes, undocumented configurations, legacy credential management, and gaps in observability coverage

Produce and maintain operational runbooks for all platform procedures, ensuring any team member can execute them safely and independently

Peer‑review all platform infrastructure changes produced by your engineering counterpart, providing challenge and quality assurance across the full stack

Contribute to platform documentation and knowledge‑sharing, supporting the wider team’s understanding of the platform as it matures

Required Skills

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 gateway engineering — production deployment and operation of an API or AI gateway (Kong, Envoy, or equivalent); rate limiting, plugin/policy authoring, route management

Linux platform engineering — networking fundamentals, TLS and PKI, CSI storage operations, container runtime

Job Information Platform Engineer, AI • Pavia, Lombardia, IT

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