We are seeking a Strategic Cloud Engineer to operate at the intersection of GCP FinOps (cost optimisation), Kubernetes platform engineering, operational excellence and agentic-AI-driven development.This is a high-leverage engineering role responsible for optimising cloud spend, increasing platform reliability, and accelerating engineering output through automation and AI-assisted workflows. You will work across GCP, Kubernetes, Terraform and modern AI-assisted development environments. The environment is a high-scale, regulated, multi-product SaaS platform.What you'll ownGCP cost optimisation & FinOps — rightsizing, autoscaling and workload efficiency; cost-observability dashboards (Grafana / BigQuery / billing exports); partnering with engineering teams to architect cost-efficient solutionsKubernetes platform operations (GKE / multi-cluster) — cluster lifecycle, upgrades, node pools and scaling; ingress and domain routing; secrets, environment variables and service deployments; multi-tenant SaaS customer lifecycle eventsInfrastructure as code & automation — own and evolve Terraform provisioning; support and optimise GitLab CI/CD rollout pipelines; automate customer provisioning and environment configurationPlatform services & observability — VictoriaMetrics, StatsD, Grafana, Google Cloud Monitoring, Elasticsearch / OpenSearch, Apache Airflow; troubleshooting distributed systems and production incidentsGlobal operations & reliability — instance provisioning and decommissioning, domain mapping, infrastructure support across providers including Hetzner, and participation in an on-call rotationAgentic-AI engineering enablement — using AI-assisted tools (Cursor, OpenCode, multi-model AI development workflows) to accelerate infrastructure development, automate operational runbooks, and improve debugging of CI/CD pipelines and distributed systemsCore technical requirementsStrong expertise in Google Cloud Platform — GKE, Compute Engine, IAM, networkingProven experience with Terraform in production environments3+ years managing production Kubernetes — cluster lifecycle and upgrades, ingress / reverse-proxy configuration, secrets and configuration managementDemonstrated ability to optimise cloud spend in production — cost allocation and usage patterns, rightsizing and scaling strategies, and building cost-visibility dashboardsGitLab CI/CD (preferred) or GitHub Actions, with the ability to debug pipelines and support release workflowsGrafana / VictoriaMetrics / StatsD / Google Cloud Monitoring; Elasticsearch / OpenSearch; Apache Airflow / Airbyte / n8nScripting in Python, Bash or similarAgentic AI development (required baseline) — working familiarity with AI-native IDEs such as Cursor and agent-based development environments, and the ability to use AI to generate, review and optimise infrastructure codeNice to haveMulti-cloud exposure (AWS / Azure)Experience in regulated environments (SOC 2, ISO)Exposure to European infrastructure providers (Hetzner)Experience building internal developer platforms (IDP)
24 days ago