Field Solutions Architect II – Generative AI – Google Cloud
| Company | |
|---|---|
| Location | Cambridge, MA, USA, Seattle, WA, USA, Houston, TX, USA, Washington, DC, USA, San Francisco, CA, USA, Austin, TX, USA, Los Angeles, CA, USA, Miami, FL, USA, Irvine, CA, USA, Chicago, IL, USA, Charlotte, NC, USA, Kirkland, WA, USA, Reston, VA, USA, New York, NY, USA, Sunnyvale, CA, USA, Mountain View, CA, USA, Boulder, CO, USA, Atlanta, GA, USA, San Diego, CA, USA, Addison, TX, USA |
| Salary | $147000 – $216000 |
| Type | Full-Time |
| Degrees | Bachelor’s |
| Experience Level | Senior |
Requirements
- Bachelor’s degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
- 5 years of experience in 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.
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.
Preferred Qualifications
- Master’s degree in Computer Science, Engineering, or a related technical field.
- Experience training and fine tuning models in large scale environments (e.g., 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.
Benefits
-
No information provided on Benefits.