AI/ML Field Solutions Architect

Minimum qualifications:

• Bachelor's degree in Computer Science, Data Science, or equivalent practical experience.
  
• 4 years of experience working in AI/ML as a technical sales engineer or in software engineering.
  
• Experience with Python and ML frameworks (e.g., TensorFlow, PyTorch).
  
• Experience delivering technical presentations and leading business value sessions.
  
• Generative AI experience as a user or a developer.
  

Preferred qualifications:

• Experience designing and deploying with one or more from the following ML frameworks: TensorFlow, PyTorch, JAX, Spark ML, etc.
  
• Experience training and fine tuning models in large-scale environments (i.e., image, language, recommendation) with accelerators.
  
• Experience with distributed training and optimizing performance versus costs.
  
• Experience with CI/CD solutions in the context of MLOps and LLMOps, including automation with IaC (e.g. using terraform).
  
• Experience in systems design with the ability to architect and explain data pipelines, ML pipelines, and ML training and serving approaches.
  

About the job

The Google Cloud Platform team helps customers transform and build what's next for their business — all with technology built in the cloud. Our products are developed for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers — developers, small and large businesses, educational institutions and government agencies — see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.

As an AI/ML Field Solutions Architect, you will support Google Cloud sales teams and engineering incubate, pilot, and deploy Google Cloud’s industry leading AI/ML and GenAI technology at AI natives and innovators, large enterprises, and early stage AI startups. You will help customers innovate faster with solutions using Google Cloud’s flexible and open infrastructure including AI Accelerators.

In this role, you will identify, assess, and develop GenAI and AI/ML applications by applying key industry tools, techniques, and methodologies to solve problems. You will help customers leverage accelerators within their overall cloud strategy by helping run benchmarks for existing models, finding opportunities to use accelerators for new models, developing migration paths, and helping to analyze cost to performance. Along the way, you would work closely with internal Cloud AI teams to remove roadblocks and shape the future of our offerings.

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.

In this role, you will identify, assess, and develop GenAI and AI/ML applications by applying key industry tools, techniques, and methodologies to solve problems. You will help customers leverage accelerators within their overall cloud strategy by helping run benchmarks for existing models, finding opportunities to use accelerators for new models, developing migration paths, and helping to analyze cost to performance. Along the way, you would work closely with internal Cloud AI teams to remove roadblocks and shape the future of our offerings.

Responsibilities

• Be a trusted advisor to our customers by understanding the customer’s business process and objectives. Architect AI-drive spanning data, AI and infrastructure, and work with peers to include the full Cloud stack into overall architecture.
  
• Demonstrate how Google Cloud is differentiated by working with customers on POCs, demonstrating features, tuning models, optimizing model performance, profiling, and benchmarking. Troubleshoot and find solutions to issues on training/serving models in a large-scale environment.
  
• Build repeatable technical assets such as scripts, templates, reference architectures, etc. to enable other customers and internal teams. Work cross-functionally to influence Google Cloud strategy and product direction at the intersection of infrastructure and AI/ML by advocating for enterprise customer requirements.
  
• Coordinate regional field enablement with leadership and work closely with product and partner organizations on external enablement activities.
  
• Travel as needed.