Platform Deployment Engineer (AI Factory Delivery)

**🗓️ Full-time Permanent**

📍 Remote, in the same time zone as the rest of the squad

🏡 Remote working

About Sharon AI

Sharon AI is an Australian neocloud, delivering trusted AI infrastructure organisations need to build, train and run AI at scale. We support customers across the full AI lifecycle, from training through to inference and agentic AI, drawing on a strong ecosystem of technology and co-location partners to deliver capability where it’s needed. As the first neocloud to join NVIDIA’s AI Compute Program, we’re growing quickly, scaling our AI Factory platform to meet rising demand for advanced compute.

The Role

As Sharon AI continues to grow, we’re looking for a talented Platform Deployment Engineer to join our regional Delivery squad and play an important role in deploying and delivering the platform layer of our AI Factories, reporting to our Regional Delivery Manager.

AI Factories are the high-performance GPU compute environments that underpin Sharon AI’s AI cloud, serving customers and internal teams running large-scale training, fine-tuning and inference. In this role, the Platform Deployment Engineer will take the orchestration, scheduling and self-service layer of each AI Factory from build through to customer acceptance, so the platform is ready for customers and for Operations from day one. You’ll both deploy and test, and look for ways to make each deployment faster than the last. You’ll work closely with Network and Infrastructure Deployment Engineers, on-site Data Centre Deployment Engineers, the Platform team and Operations, with exposure to cutting-edge technology at the forefront of AI infrastructure.

What You’ll Be Doing

• Bringing each AI Factory under Rafay, and standing up and tuning Kubernetes clusters (and Slurm where used) with GPU nodes to meet agreed design and performance targets
• Deploying the NVIDIA GPU Operator and other platform components at the version-locked set
• Configuring workload scheduling, resource allocation, and multi-tenant security and isolation in line with each AI Factory’s design
• Configuring and validating self-service APIs, CLI and portal access for GPU provisioning, and integrating observability and monitoring so each AI Factory is visible to Operations from day one
• Planning and executing platform testing, including acceptance testing (ATP), integrated system testing (IST), GPU performance and NCCL benchmarks, and multi-hour training jobs across the cluster
• Supporting customer site acceptance testing, recording results against pass/fail thresholds and fixing defects before handover
• Working with on-site staff and the data centre provider’s remote hands for any physical task, such as reseating, part swaps or console access
• Delivering repeatable deployments using infrastructure-as-code (Ansible and Terraform) and shared deployment scripts, timing each step and proposing changes that shorten the cycle
• Feeding lessons learned back into standard playbooks and tooling, and to the Platform team for its designs and configurations
• Preparing handover documentation, runbooks and as-built records, and transferring knowledge to Operations and the Platform team
• Supporting Operations during the hypercare window (up to one month), on call for build defects, and reporting progress, risks and issues to the Regional Delivery Manager

What We’re Looking For

• 4 to 8+ years’ experience in platform engineering or SRE
• Strong experience with Kubernetes and GPU workloads in production
• Experience working 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
• Bachelor’s degree in Computer Science or a related field
• Strong communication and stakeholder management skills
• Ability to manage competing priorities in a fast-paced environment
• A proactive, collaborative and solutions-focused approach
• Strong attention to detail
• A genuine interest in AI, technology and what Sharon AI is building

Nice to have:

• Rafay or similar multi-cluster platform experience
• GPU cloud or HPC platform experience, and GPU technologies such as CUDA and NCCL
• ML frameworks (PyTorch, TensorFlow) and MLOps tools (Ray, Kubeflow, Airflow)
• CKA or cloud certifications

Why Join Sharon AI?

You’ll be joining a rapidly growing Australian technology business at an exciting stage of its journey, with the opportunity to…