Platform & Kubernetes Engineer (K8s, CI/CD, GitOps)

Selby Jennings

Singapore

On-site

SGD 120,000 - 180,000

Full time

14 days+

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Job summary

Selby Jennings in Singapore seeks a hands-on DevOps/Platform Engineer to design, build, and operate a container and cloud platform in production. You will own the full Kubernetes lifecycle, from provisioning and configuration to upgrades, security, backup, and recovery, across development, testing, staging, and production environments.

The role emphasizes automation, infrastructure as code (Terraform, Pulumi, CloudFormation), GitOps, and strong observability.

Qualifications

Hands-on DevOps/Platform Engineering background with production Kubernetes experience.
Experience provisioning, configuring, upgrading, securing, and operating Kubernetes clusters.
Strong expertise in Infrastructure as Code (Terraform, Pulumi, CloudFormation) and automation.
Proficiency in CI/CD and GitOps tooling (GitHub Actions, GitLab CI, Jenkins, Argo CD, Flux).
Familiarity with observability tools (Prometheus, Grafana, OpenSearch, Datadog, ELK) and security baselines.

Responsibilities

Design and build Kubernetes clusters across development, testing, staging, and production.
Provision and configure clusters using managed services or self-managed distributions.
Own full lifecycle: installation, configuration, scaling, patching, upgrades, backup, recovery, decommissioning.
Manage cluster capacity, availability, and performance.
Configure networking, ingress, DNS, load balancing, storage, and PVs.
Implement security controls (RBAC, secrets, policies, image security).
Develop reusable platform services and automation using IaC.
Establish governance standards and production-readiness requirements.
Create templates, Helm charts, platform blueprints, and golden paths.
Build CI/CD and GitOps capabilities with GitHub Actions, GitLab CI, Jenkins, Argo CD, or Flux.
Manage container registries, image lifecycle, vulnerability scanning, and supply-chain controls.
Collaborate with security and engineering teams for governance and compliance.
Support production incidents and root-cause analysis.
Evaluate new platform technologies for scale, security, reliability, and productivity.
Support AI/ML workloads with GPU clusters and model deployment where applicable.