Field Solutions Architect IV, AI Infrastructure, Google Cloud
Google· startup· Internet & Cloud Services· Sep 28, 2026 · 1mo ago
Sunnyvale, CA, USA · Atlanta, GA, USA · Chicago, IL, USA · Kirkland, WA, USA · Austin, TX, USA · New York, NY, USA
In office
Full-time
Principal
$177K–$239K est.
10+ years exp
Job description
As an AI Infrastructure Field Solution Architect, your experience and thought leadership will support Google Cloud commercial teams to incubate, pilot, and roll out industry-leading TPU and GPU hardware at market innovators, large enterprises, and early-stage startups. You will empower accounts to innovate faster with systems and software using our latest technical offerings.
You will guide technical discussions on network topologies, fabric architecture, and compute or storage integration, while supporting server and cluster provisioning. This includes on-site visits to client hosting facilities during physical implementation.
You will identify and assess opportunities, evaluating cost-to-performance tradeoffs, evaluating baseline models, developing migration paths, and integrating specialized silicon into overarching technical roadmaps. Along the way, you will partner closely with internal product and engineering units to remove roadblocks and shape future platform capabilities.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $233000 - $324000 (USD) + 25% bonus target + equity + benefits
Learn more about benefits at Google .
• Act as a trusted advisor to customers, helping them understand and incorporate AI accelerators into their overall business strategy by designing training and inferencing platforms, and incorporating customers' existing data center technologies.
• Demonstrate how Google Cloud is differentiated, highlighting the power of accelerators by working with customers on Proofs of Concept, demonstrating features, optimizing model performance, profiling, and benchmarking.
• Assist in production deployment of Google Cloud accelerators, troubleshoot integration issues with existing compute, networking, and storage solutions, and enable customers to operate their training and inferencing clusters successfully.
• Build repeatable assets to enable other customers and internal teams.
• Collaborate with the AI Infrastructure Dedicated Engineering Team in Google Compute Engine, and scale ML techniques and solutions broadly. Influence Google Cloud strategy at the intersection of infrastructure and AI by advocating for enterprise customer requirements.
• Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
• 10 years of experience with data center technologies, solutions and their deployment..
• Experience with data center networking, such as routing, switching, DNS, and IP addressing; compute and storage concepts; and operations including power, cooling, and procedures.
• Experience with AI model training and inference, deploying OSS frameworks such as PyTorch, Jax, etc., and optimizing performance versus costs.
• Experience with scripting and coding in Python, YAML, and Kubernetes.
Minimum qualifications:
• Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
• 10 years of experience with data center technologies, solutions and their deployment..
• Experience with data center networking, such as routing, switching, DNS, and IP addressing; compute and storage concepts; and operations including power, cooling, and procedures.
• Experience with AI model training and inference, deploying OSS frameworks such as PyTorch, Jax, etc., and optimizing performance versus costs.
• Experience with scripting and coding in Python, YAML, and Kubernetes.
Preferred qualifications:
• Experience training and fine tuning large models with accelerators.
• Experience with performance profiling and benchmarking tools (i.e., MLPerf, PyTorch profiler, Tensorboard).
• Experience deploying compute, network and storage solutions in data centers.
• Experience deploying TPU clusters.
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aimlpythonkubernetes
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