Senior AI Infrastructure Field Architect

Google LLC; Austin (TX); On-site; USD 233,000 - 324,000; Full time

Benefits offered by this job

Health insurance

401(k) with company match

Paid time off 20 days

Sick time 40 hours/year

Maternity leave 28-30 weeks

Baby bonding leave 18 weeks

13 paid holidays

Job summary

Google LLC is seeking an AI Infrastructure Field Solution Architect to enable Google Cloud customers to adopt TPU and GPU accelerators across enterprises. The role involves leading architecture discussions, designing training and inference pipelines, and coordinating with customer teams to ensure seamless deployment.

You will guide network fabric, compute/storage integration, and on-site field work, while partnering with product/engineering to remove roadblocks and shape future platform

Qualifications

• Bachelor's degree in STEM or equivalent practical experience.
• 10 years of experience with data center technologies, solutions and their deployment.
• Experience with data center networking, compute and storage concepts.
• Experience with AI model training and inference using OSS frameworks (e.g., PyTorch, JAX).
• Experience with scripting and coding in Python, YAML, and Kubernetes.

Responsibilities

• Act as trusted advisor to customers, designing training/inference platforms and integrating data center tech.
• Demonstrate Google Cloud accelerators, run Proofs of Concept, profile/model performance, benchmark.
• Assist in production deployment of Google Cloud accelerators and troubleshoot integration with compute, networking, storage.
• Build repeatable assets to enable customers and internal teams.
• Collaborate with AI Infrastructure Engineering Team to scale ML solutions and align with enterprise needs.

Skills

AI model training

Python scripting

Kubernetes

Education

Bachelor's degree in STEM

Tools

PyTorch

JAX

Kubernetes

Job description

Google LLC is seeking an AI Infrastructure Field Solution Architect to enable Google Cloud customers to adopt TPU and GPU accelerators across enterprises. The role involves leading architecture discussions, designing training and inference pipelines, and coordinating with customer teams to ensure seamless deployment.

You will guide network fabric, compute/storage integration, and on-site field work, while partnering with product/engineering to remove roadblocks and shape future platform