Platform Deployment Engineer (AI Factory Delivery)

Sharon AI, Inc; Sydney; Remote; AUD 120,000 - 180,000; Full time

Benefits offered by this job

Remote work in same time zone

Birthday leave

Learning & development

Novated leasing

Employee of the month

Job summary

Sharon AI seeks a Platform Deployment Engineer to deploy and manage the AI Factory platform layer. You will orchestrate, test and optimize deployments, ensuring ready-for-operations GPU compute environments.

Join a rapid-growing Australian neocloud at the forefront of AI infrastructure, with remote work within the same time zone and a culture focused on collaboration and innovation.

Qualifications

• Bachelor's degree in Computer Science or a related field.
• 4 to 8+ years' experience in platform engineering or SRE.
• Strong experience with Kubernetes and GPU workloads in production.
• Experience with cloud-native infrastructure at scale.
• Proficiency in Ansible and Terraform, and programming in Python, Go or Bash.
• Experience with CI/CD and infrastructure automation.
• 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 Sharon AI's mission.

Responsibilities

• Bring each AI Factory under Rafay, stand up and tune Kubernetes clusters with GPU nodes to meet design 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.
• Configure and validating self-service APIs, CLI and portal access for GPU provisioning, and observability for Operations from day one.
• Plan and execute platform testing: ATP, IST, NCCL benchmarks, multi-hour training jobs.
• Support customer site acceptance testing and fix defects before handover.
• Coordinate with on-site staff and data centre for physical tasks.
• Deliver repeatable deployments using Ansible and Terraform; optimize deployment cycles.
• Feed lessons into standard playbooks and Platform team workflows.
• Prepare handover docs, runbooks and transfer knowledge to Operations.

Skills

Kubernetes

GPU workloads

Cloud-native infra

Ansible

Terraform

Python

Go

Bash

CI/CD

Education

Bachelor's degree in Computer Science or related field

Tools

Rafay

CUDA

NCCL

PyTorch

TensorFlow

Job description

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

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, and 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 work directly with the infrastructure and technology powering the next generation of AI.

Remote working – work from wherever suits you, in the same time zone as your squad

Birthday leave – take some extra time to celebrate your day

Employee Assistance Program (EAP) – confidential support when you need it

Exposure to AI and next-generation technology – work in one of the fastest-moving areas of technology

Learning & development – we support your career growth with approved conferences, professional memberships & courses

Novated leasing – a tax-effective way to finance and run your car

Bounty referral program – generous rewards for successfully referring new talent to Sharon AI

Employee of the month – recognition plus a $500 gift card

Growing global business – be part of an Australian technology company with an expanding international footprint

Make an impact – join at a stage where your work can have a visible influence on how we build and grow

Collaborative culture – work alongside talented people across technical, commercial and corporate teams

Our Values

Integrity | Innovation | Collaboration | Wellbeing | Inclusion

Due to the high volume of applications we receive, we're unfortunately not always able to provide individual feedback to unsuccessful candidates. We appreciate your understanding and want to assure you that every application will be reviewed with care.