Field Solutions Architect I, Generative AI, Google Cloud

Google - Houston, TX

Full-time - Entry Level

Houston, TX

5,001-10,000 employees

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About the position

As a Field Solutions Architect I specializing in Generative AI at Google Cloud, you will support the sales organization by developing rapid prototype applications tailored to a diverse clientele. Your role involves leveraging cutting-edge Generative AI technologies to create innovative solutions, collaborating closely with customers to showcase capabilities, and working with product teams to shape future offerings. You will be instrumental in driving digital transformation for organizations by delivering enterprise-grade solutions on the Google Cloud platform.

Responsibilities

• Be a trusted advisor to our customers by understanding the customer's business process and objectives.
• Design Generative AI-driven solutions, spanning AI, Data 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 application prototypes, demonstrating Generative AI features, prompting and tuning models, optimizing model performance, profiling, and benchmarking.
• Troubleshoot and find solutions to issues in Generative AI applications.
• Build repeatable technical assets (i.e., scripts, templates, reference architectures, etc.) to enable 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.
• Travel as needed.

Requirements

• Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
• 3 years of experience working with a statistical programming language (e.g. Python).
• Experience in Artificial Intelligence applications (e.g., deep learning, natural language processing, computer vision, or pattern recognition), applied machine learning techniques, or using OSS frameworks (e.g., TensorFlow, PyTorch).
• Experience delivering technical presentations and leading business value sessions.

Nice-to-haves

• Master's degree in Computer Science, Engineering, or a related technical field.
• Experience with distributed training and optimizing performance versus costs.
• Experience training and fine tuning models in large-scale environments (e.g., image, language, recommendation) with accelerators.
• 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.