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Google Field Solutions Architect III Generative AI Google Cloud United States, California, San Francisco 943528056
28.04.2025
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Info This role may also be located in our Playa Vista, CA campus.Applicants in the County of Los Angeles: Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.Note: By applying to this position you will have an opportunity to share your preferred working location from the following: San Francisco, CA, USA; Los Angeles, CA, USA; Irvine, CA, USA.
This role may also be located in our Playa Vista, CA campus.
Applicants in the County of Los Angeles: Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following:.- 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 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 training and fine tuning models in environments (e.g., image, language, recommendation) with accelerators.
- Experience in systems design with the ability to architect and explain data pipelines, Machine Learning pipelines, and Machine Learning training and serving approaches.
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