SalaryPeak

AI Infrastructure engineer

ITCAN PTE. LIMITED
Singapore 3+ years Posted Mar 17, 2026

Salary Range

SGD 72,000 - SGD 96,000 /year

SGD 6,000 - SGD 8,000/month

Skills Required

TroubleshootingCloud AdministrationCloud ApplicationsKubernetesPipeline ManagementGPUArtifactoryPrivate CloudEnterprise ArchitectAutomation DesignWindows RegistryCUDAContainerizationAI accelerator

Job Description

Job Description

AI Infrastructure Specialist

About the Role

We are building a next-generation Enterprise AI Platform to power large-scale AI workloads across hybrid environments — and we’re looking for an AI Infrastructure Specialist to lead and scale our AI computing ecosystem.

This role will oversee AI workloads across:

•                     On-prem GPU clusters

•                     Public cloud platforms

•                     Private AI cloud environments

•                     Edge / distributed sites

Key Responsibilities

•                     Design and manage hybrid AI infrastructure (GPU clusters, Kubernetes, private & public cloud).

•                     Optimize high-performance compute environments (NVIDIA GPUs, CUDA, NVLink, AI accelerators).

•                     Enable scalable ML training and inference platforms.

•                     Implement containerized AI environments and orchestration.

•                     Support MLOps pipelines and model lifecycle management.

•                     Enable CI/CD for ML deployments.

•                     Integrate model registry, artifact storage, and observability tools.

•                     Manage vector databases and inference endpoints.

•                     Ensure secure, resilient, and compliant AI infrastructure.

•                     Drive cost optimization, performance tuning, and capacity planning.

•                     Support AI use cases such as Generative AI, Vision AI, RAG pipelines, and predictive analytics.

Requirements

•                     3+ years in Infrastructure, cloud engineering.

•                     Hands-on experience with GPU clusters and Kubernetes.

•                     Strong knowledge of containerization and orchestration platforms.

•                     Experience operating in hybrid or multi-cloud environments.

•                     Automation & Infrastructure-as-Code mindset.

•                     Strong troubleshooting and systems thinking skills.

•                     Passion for enabling enterprise AI at scale.