Platform Deployment Engineer; AI Factory Delivery Job Abu Dhabi UAE/Dubai,IT/Tech

Position: Platform Deployment Engineer (AI Factory Delivery)

️ Full-time | Permanent | Start ASAP

United Arab Emirates (UAE) – Remote

UAE-based remote working

About Sharon AI

Sharon AI is a neocloud, delivering trusted AI infrastructure organisations need to build, train and run AI 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 UAE 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…