Platform Deployment Engineer (AI Factory Delivery)

About the role

Sharon AI is an Australian neocloud provider of AI infrastructure, and it is looking for a Platform Deployment Engineer to join its regional Delivery squad. The role reports to the Regional Delivery Manager and covers the orchestration, scheduling and self-service layer of AI Factories, from build through to customer acceptance. AI Factories are high-performance GPU compute environments that power Sharon AI's AI cloud.

Key responsibilities

• Bring each AI Factory under Rafay and stand up and tune Kubernetes clusters (and Slurm where used) with GPU nodes to meet design and performance targets.
• Deploy the NVIDIA GPU Operator and other platform components at the version-locked set.
• Configure workload scheduling, resource allocation and multi-tenant security and isolation.
• Set up self-service APIs, CLI and portal access for GPU provisioning, and integrate observability and monitoring.
• Plan and run platform testing, including acceptance testing, integrated system testing, GPU performance and NCCL benchmarks, and multi-hour training jobs.
• Support customer site acceptance testing, record pass/fail results and fix defects before handover.
• Work with on-site staff and the data centre provider's remote hands on physical tasks such as reseating and part swaps.
• Deliver repeatable deployments using infrastructure-as-code (Ansible and Terraform) and shared scripts, and feed lessons learned back into playbooks and tooling.
• Prepare handover documentation, runbooks and as-built records, and support Operations during the hypercare window (up to one month).

Required profile

• 4 to 8+ years' experience in platform engineering or SRE.
• Bachelor's degree in Computer Science or a related field.
• Strong communication and stakeholder management skills, and ability to manage competing priorities in a fast-paced environment.
• A proactive, collaborative, solutions-focused approach and strong attention to detail.

Required skills

• Strong experience with Kubernetes and GPU workloads in production.
• Experience with cloud-native infrastructure at scale.
• Strong experience with Ansible and Terraform, and programming in Python, Go or Bash.
• Experience with CI/CD and infrastructure automation.
• Nice to have: Rafay or similar multi-cluster platform experience, GPU cloud or HPC experience, CUDA and NCCL, ML frameworks (PyTorch, TensorFlow) and MLOps tools (Ray, Kubeflow, Airflow), CKA or cloud certifications.

What we offer

• Remote working in the same time zone as the squad.
• Birthday leave and an Employee Assistance Program.
• Learning and development support, including conferences, memberships and courses.
• Novated leasing and a bounty referral program.