We are looking for a Field Solutions Architect III to join our team
Minimum qualifications:
- Bachelor’s degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
- 7 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.
Preferred qualifications:
- Master’s degree in Computer Science, Engineering, or a related technical field.
- Experience with CI/CD solutions in the context of ML Operations and Large Language Model (LLM) Operations including automation with IaC (e.g., Terraform).
- Experience in systems design with the ability to architect and explain data pipelines, ML pipelines, and ML training and serving approaches.
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Experience training and fine tuning models in large scale environments (e.g., image, language, recommendation) with accelerators.
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Experience with distributed training and optimizing performance versus costs.
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 cloudstack into overall architecture.
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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.
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Build repeatable technical assets (i.e., scripts, templates, reference architectures, etc.) to enable customers and internal teams.
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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.
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