DevOps Architect

RDT; Brande; A distancia; DKK 673.000 - 972.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Remote within EU

Flexible working hours

Career development opportunities

Descripción de la vacante

RDT is seeking a Senior Platform Engineer to design and scale an Internal Developer Platform, focusing on DevSecOps, GitOps delivery, and AI-enabled operations across multi-cluster Kubernetes environments. You will lead architecture, automation, and policy-as-code practices from day one.

The role covers SLO/SLI governance, automated remediation, and cross-team RFCs, while supporting AI inference workloads and GPU autoscaling. Remote within EU.

Formación

• 6+ years of enterprise-scale Kubernetes in production.
• Proficiency in Go and Python for automation.
• Experience with GitOps delivery and multi-cluster setups.
• Experience with policy as code and software supply chain security.
• Experience with IaC, CI/CD and telemetry standards.
• Experience with AI infra is a plus.

Responsabilidades

• Design and standardize a self-service Internal Developer Platform with ephemeral environments.
• Architect multi-cluster, multi-region GitOps delivery with ArgoCD or Flux.
• Build closed-loop remediation and AIOps pipelines using OpenTelemetry, anomaly correlation and workflow/orchestration engines.
• Implement policy as code and software supply chain security with Kyverno or OPA.
• Define SLO/SLI governance, error budgets and automated chaos testing for distributed services.
• Establish standards for IaC, CI/CD and telemetry, and drive them through cross-team RFCs and architecture reviews.
• Support inference and training infrastructure for AI workloads, including GPU autoscaling (KEDA).

Conocimientos

Kubernetes

Go

Python

GitOps

OpenTelemetry

Terraform

Herramientas

ArgoCD

Flux

Pulumi

OpenTofu

Kubernetes Operators

Descripción del empleo

Senior DevOps & Platform Engineer (Internal Developer Platform, DevSecOps, AIOps)

Location: Remote within EU

We are looking for a Senior / Lead Platform & DevOps Engineer with 8+ years of experience to design, build and scale an Internal Developer Platform, enterprise GitOps delivery and automated remediation across multi-cluster Kubernetes environments. The role sits in Integration Testing & Release DevOps and requires strong hands-on depth from day one.

Responsibilities

• Design and standardize a self-service Internal Developer Platform (e.g. Backstage or Port) with golden deployment paths and secure, **ephemeral environments**
• Architect multi-cluster, multi-region GitOps delivery with ArgoCD or Flux, including automated canary analysis, traffic shadowing and automated rollbacks
• Build closed-loop remediation and AIOps pipelines using OpenTelemetry, anomaly correlation and workflow/orchestration engines (Temporal, Kubernetes Operators in Go)
• Implement policy as code and software supply chain security with Kyverno or OPA, SAST/SCA gates, cosign and SBOMs
• Define SLO/SLI governance, error budgets and automated chaos testing for distributed services
• Establish standards for IaC, CI/CD and telemetry, and drive them through cross-team RFCs and architecture reviews
• Support inference and training infrastructure for AI workloads, including GPU autoscaling (KEDA)

Required experience

• 6+ years of enterprise-scale Kubernetes in production: multi-cluster networking, service meshes (Istio/Linkerd), ingress controllers, CRD design
• Strong Go (custom operators, platform controllers) and Python (automation, data and AI pipelines)
• Modular Terraform, OpenTofu or Pulumi, including state management, drift detection and policy testing
• GitOps and delivery tooling: ArgoCD, Flux, GitHub Actions
• Observability: OpenTelemetry collector topologies, tail-based sampling, Prometheus, Grafana (Mimir/Tempo), ClickHouse or Datadog
• Policy as code and supply chain security: Kyverno, OPA, SAST/SCA, cosign, SBOMs
• Deterministic, idempotent workflows with Temporal or event-driven architectures on Kafka
• Track record of authoring cross-team RFCs and defining operational SLOs/SLIs
• Cloud: AWS and/or GCP
• Incident tooling: PagerDuty, Incident.io, Slack API

Nice to have

• Integration of ITSM/CMDB platforms (iTop or similar) into automated incident workflows
• Experience integrating LLM tool-calling, agentic triage or RAG-based runbook retrieval into incident response
• AI inference infrastructure (Ray, vLLM, Triton, Kubeflow)
• Contributions to CNCF/open-source projects