Network 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 Network Deployment Engineer to join our regional Delivery squad and play an important role in deploying and delivering the network 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 Network Deployment Engineer will take the network and GPU fabrics of each AI Factory from preparation through build, validation and platform deployment to customer acceptance, with a strong focus on NVIDIA SuperPOD reference architectures, RDMA and InfiniBand. The role is focused on delivery rather than day-to-day operations: you'll both deploy and test, and look for ways to make each deployment faster than the last. You'll work closely with Infrastructure and Platform Deployment Engineers, on-site Data Centre Deployment Engineers, the Platform and Infrastructure teams and Operations, with exposure to cutting-edge technology at the forefront of AI infrastructure.

What You'll Be Doing

• Bringing switches under Netris using the standard configuration
• Deploying and configuring InfiniBand fabrics and leaf-spine (Clos) Ethernet topologies based on NVIDIA SuperPOD reference designs, including BGP and EVPN/VXLAN where used
• Configuring and tuning RDMA networking (RoCE and InfiniBand verbs) to meet agreed design and performance targets for distributed training
• Implementing network segmentation, redundancy and fault isolation for multi-tenant environments, and working with platform engineers on network-aware scheduling in Kubernetes or Slurm
• Directing on-site staff and the provider's remote hands to find and fix optics and cabling faults, and checking link health, optics and error counters across the fabric
• Planning and executing network testing, including acceptance testing (ATP), integrated system testing (IST), fabric validation, RDMA bandwidth and latency tests, and NCCL benchmarking at increasing scale
• Supporting fabric soak and cluster-level testing (L12), and working with GPU and switch vendors to fix defects before handover
• Delivering repeatable deployments using infrastructure-as-code (Ansible, 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 and Infrastructure teams
• Integrating network monitoring and telemetry (latency, congestion, packet loss) so each AI Factory is visible to Operations from day one
• Preparing handover documentation, runbooks and as-built records, supporting Operations during the hypercare window (up to one month), and reporting progress, risks and issues to the Regional Delivery Manager

What We're Looking For

• 4 to 8+ years' experience in network engineering, preferably in HPC or cloud environments
• Hands-on experience with InfiniBand and/or RDMA networking
• Experience deploying and operating large-scale data centre network fabrics
• Experience supporting GPU or AI/ML infrastructure at scale
• Familiarity with NCCL, MPI and distributed training communication patterns
• Automation and scripting skills (Python, Bash)
• Bachelor's degree in Computer Science, Electrical Engineering 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:

• NVIDIA SuperPOD or DGX-based deployment experience
• Netris or other software-defined networking platforms
• RoCEv2 and QoS tuning in Ethernet-based AI clusters, and storage networking (NVMe-oF, GPUDirect Storage)
• CCNP/CCIE, JNCIP/JNCIE, NVIDIA Networking or similar certifications
• Experience in GPU cloud or neocloud environments

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

Apply today and help us build the infrastructure powering the next generation of AI.

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.