Remote AI Factory Platform Engineer – Kubernetes & GPU

Sharon AI, Inc; Singapore; Remote; SGD 120,000 - 180,000; Full time

Benefits offered by this job

Birthday leave

Novated leasing

EAP – Employee Assistance Program

Bounty referral program

Learning & development support

Job summary

Sharon AI, Inc is seeking a Platform Deployment Engineer to join the regional Delivery squad. You will deploy and operate the AI Factory platform layer, standing up GPU-enabled Kubernetes clusters, integrating self-service APIs, and coordinating with on-site data centre teams to ensure smooth handover to Operations.

You will work with Rafay, Slurm, CUDA/NCCL, and industry-standard tools, driving repeatable deployments and documentation while engaging with cross-functional teams to optimize

Qualifications

• 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

Responsibilities

• 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

Skills

Kubernetes

GPU workloads

cloud-native infra

Ansible

Terraform

Python

Go

Bash

CI/CD

infrastructure automation

Education

Bachelor's degree in Computer Science

Tools

Rafay

CUDA

NCCL

PyTorch

TensorFlow

Kubeflow

Ray

Airflow

Job description

Sharon AI, Inc is seeking a Platform Deployment Engineer to join the regional Delivery squad. You will deploy and operate the AI Factory platform layer, standing up GPU-enabled Kubernetes clusters, integrating self-service APIs, and coordinating with on-site data centre teams to ensure smooth handover to Operations.

You will work with Rafay, Slurm, CUDA/NCCL, and industry-standard tools, driving repeatable deployments and documentation while engaging with cross-functional teams to optimize