AI Storage Engineer — High-Throughput Cluster Commissioning

Firmus Technologies Pty Ltd.; City of Melbourne; On-site; AUD 120,000 - 180,000; Full time

Job summary

Firmus Technologies is seeking a Storage Engineer to join our Commissioning team in Australia. You will deploy, configure, and test high-performance storage systems for AI infrastructure projects, collaborating with the Global Operations Centre and Solution Architects to ensure scalable, efficient data management.

The role requires 5+ years of experience, strong Linux administration, and the ability to work across AI workloads.

Qualifications

• Bachelor’s degree in IT, software engineering, CS or related field.
• 5+ years deploying, operating, or commissioning enterprise, HPC, cloud, or AI storage infrastructure.
• Experience with scale-out storage platforms such as VAST Data, Weka, GPFS, PowerScale, Ceph or Lustre.
• Experience with block, file, and object storage systems and protocols incl. NFS, SMB, S3, iSCSI, NVMe-oF.

Responsibilities

• Deploy, configure, monitor, optimise, and troubleshoot high-performance storage systems.
• Configure, test, diagnose, and benchmark performance of storage solutions.
• Analyse logs, coordinates with teams/vendors; maintain monitoring tools.
• Develop automated storage configurations using IaC; implement CI/CD for changes.

Skills

Linux administration

Storage systems

HPC storage

CI/CD

Infrastructure as Code

Project management

Troubleshooting

Communication skills

Networking

Education

Bachelor’s degree in IT/CS

Tools

VAST Data

Weka

IBM Storage Scale (GPFS)

Dell PowerScale

Ceph

Lustre

Job description

Firmus Technologies is seeking a Storage Engineer to join our Commissioning team in Australia. You will deploy, configure, and test high-performance storage systems for AI infrastructure projects, collaborating with the Global Operations Centre and Solution Architects to ensure scalable, efficient data management.

The role requires 5+ years of experience, strong Linux administration, and the ability to work across AI workloads.