AI Infrastructure Solutions Engineer, Compute

Google LLC

Singapore

On-site

SGD 120,000 - 180,000

Full time

2 days ago
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Job summary

Google Cloud seeks a Customer Solutions Engineer to own complex AI infrastructure issues and provide 24x7 support for AI/ML workloads. You will diagnose hardware/software problems, triage across the stack, and help improve internal tools and documentation.

The role emphasizes collaboration with engineering, SRE, and product teams to resolve deployment challenges. The position requires experience with Linux/Unix, AI hardware, ML frameworks, and on-prem or cloud environments; some on-call, shift,

Qualifications

  • Bachelor's degree or equivalent practical experience in STEM fields.

Responsibilities

  • Manage customer problems through diagnosis, resolution, or implementation of new investigation tools.
  • Develop understanding of AI/ML workloads and hardware architectures by troubleshooting and building faster diagnosis tools.
  • Act as consultant and SME for internal stakeholders to resolve deployment/operational obstacles in AI infrastructure environments.
  • Collaborate with Product/Engineering teams to improve product and interact with SRE for production quality.
  • Participate in rotating on-call schedules including nights, weekends and holidays.

Education

Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience
Linux/Unix system administration
Troubleshooting across hardware/software stack
AI/ML hardware experience ( GPUs/accelerators )
ML frameworks (TensorFlow, PyTorch)
Distributed systems
Containerization/orchestration (Kubernetes, Slurm)

Tools

Kubernetes
Slurm

Job description

Google Cloud seeks a Customer Solutions Engineer to own complex AI infrastructure issues and provide 24x7 support for AI/ML workloads. You will diagnose hardware/software problems, triage across the stack, and help improve internal tools and documentation.

The role emphasizes collaboration with engineering, SRE, and product teams to resolve deployment challenges. The position requires experience with Linux/Unix, AI hardware, ML frameworks, and on-prem or cloud environments; some on-call, shift,

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