AI Compute Operations Lead — Scale GPU Clusters

Vyntrapro Innovations; Indonesia; On-site; IDR 240,000,000 - 420,000,000; Full time

Job summary

Vyntrapro Innovations is seeking an experienced AI Compute Operations Lead to oversee the deployment, operations, and reliability of large-scale AI infrastructure, including GPU clusters and HPC environments. You will ensure high availability, performance, and scalability to support AI/ML workloads.

The role requires leadership in capacity planning, incident management, and cross-functional collaboration with engineering, network, and cloud teams to drive efficiency and security across data

Qualifications

• 5+ years of experience in Technical Program Management, Infrastructure Operations, or Data Center Management, preferably in GPU/HPC environments.
• Strong understanding of AI infrastructure, GPU computing, and large-scale distributed systems.
• Experience with Linux environments, data center operations, and cloud platforms (AWS, Azure, or GCP).
• Knowledge of capacity planning, performance monitoring, and incident management.
• Excellent stakeholder management, communication, and leadership skills.

Responsibilities

• Manage AI compute infrastructure, GPU clusters, and data center operations.
• Drive deployment, capacity planning, and performance optimization of AI platforms.
• Ensure infrastructure reliability, monitoring, incident management, and operational excellence.
• Collaborate with engineering, network, storage, and cloud teams to support business requirements.
• Lead infrastructure projects, upgrades, and vendor coordination.
• Establish best practices for security, governance, and operational efficiency.

Skills

Technical Program Mgmt

Infrastructure Operations

Data Center Mgmt

AI infrastructure

GPU HPC

Job description

Vyntrapro Innovations is seeking an experienced AI Compute Operations Lead to oversee the deployment, operations, and reliability of large-scale AI infrastructure, including GPU clusters and HPC environments. You will ensure high availability, performance, and scalability to support AI/ML workloads.

The role requires leadership in capacity planning, incident management, and cross-functional collaboration with engineering, network, and cloud teams to drive efficiency and security across data