AI Cloud Solutions Engineer

Google; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Google is hiring a Customer Solutions Engineer for Compute, Google Cloud in Singapore. This full-time, on-site role requires working in shifts and may include weekends.

The engineer will own complex customer issues, provide specialized support across teams, and help customers deploy AI/ML workloads on AI infrastructure products. Responsibilities include diagnosing issues, understanding AI workloads, advising internal stakeholders, and collaborating with multiple product/engineering teams to

Qualifications

• Bachelor's degree or equivalent practical experience.
• Experience reading/debugging code in languages such as Java, C, C++, Python, Go or JavaScript.
• Experience in Linux/Unix system administration and debugging issues across enterprise-grade server infra.
• Experience troubleshooting and triaging technical issues across the stack including hardware, software, networking, virtualization, and kernel drivers.

Responsibilities

• Diagnose and resolve AI/ML infrastructure issues for customers.
• Develop deep understanding of AI/ML workloads and underlying hardware architectures; build faster diagnosis tools.
• Serve as a consultant to internal engineering, sales, and customer teams to resolve deployment obstacles.
• Collaborate with product and engineering teams and site reliability engineering to improve production systems.
• Participate in rotating on-call schedules including nights, weekends and holidays.

Skills

Code debugging

Linux/Unix administration

Troubleshooting

AI infrastructure understanding

Kubernetes

AI hardware experience

ML frameworks (TensorFlow/PyTorch)

Distributed systems

Education

Bachelor's degree in STEM or equivalent

Tools

Kubernetes

Slurm

Linux tooling

Job description

Google is hiring a Customer Solutions Engineer for Compute, Google Cloud in Singapore. This full-time, on-site role requires working in shifts and may include weekends.

The engineer will own complex customer issues, provide specialized support across teams, and help customers deploy AI/ML workloads on AI infrastructure products. Responsibilities include diagnosing issues, understanding AI workloads, advising internal stakeholders, and collaborating with multiple product/engineering teams to