Infrastructure Engineer
Summary
Hands-on Infrastructure Engineer managing Linux systems, OpenShift/Kubernetes clusters, and video/CCTV hardware integration in Sydney.
We are looking for a hands-on Infrastructure Engineer to manage, scale, and optimise our client's core Linux systems and containerised environments while integrating high-definition camera and CCTV hardware streams. You will bridge the gap between low-level edge video inputs, automated platform scripting, and robust cloud/on-prem container Orchestration.
Contract role, based in Sydney on an initial 6 month contract, paying up to $850 inc Super per day.
Key Responsibilities
Contract role, based in Sydney on an initial 6 month contract, paying up to $850 inc Super per day.
Key Responsibilities
- Architect, deploy, and maintain enterprise OpenShift and Kubernetes clusters to support real-time media feeds.
- Build robust automation scripts using Python, Bash, or Go to automate infrastructure provisioning, monitoring, and operational workflows.
- Administer, tune, and harden enterprise Linux environments (RHEL, CentOS, Ubuntu) for high availability and performance.
- Manage the integration, routing, storage, and health monitoring of camera/CCTV hardware, RTSP/VMS streams, and video analytics edge node hardware.
- Partner with software engineers to optimise CI/CD pipelines and streamline application deployments on OpenShift.
- Linux Systems: Advanced administration, performance tuning, and networking (TCP/IP, firewalls, storage protocols).
- Container Orchestration: Solid experience building and maintaining Red Hat OpenShift clusters in production.
- Automation & Scripting: Strong proficiency in Python (or Go/Ruby/Bash) for system automation and tool development.
- Video Technology & Edge Hardware: Hands-on experience with CCTV systems, IP cameras, Video Management Systems (VMS), RTSP streaming, or video ingestion pipelines.
- Infrastructure as Code (IaC): Familiarity with Ansible, Terraform, or Puppet.
- Experience with computer vision (OpenCV) or video analytics pipelines.
- Background handling high-bandwidth network loads and distributed object/block storage for video retention.