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Google Field Solutions Architect Generative AI Google Cloud 
United States, New York, New York 
47843322

28.07.2025

שיתוף

התחבר/י כדי להגיש מועמדות
Info 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.This role may also be located in our Playa Vista, CA campus.Note: By applying to this position you will have an opportunity to share your preferred working location from the following: New York, NY, USA; Atlanta, GA, USA; Boulder, CO, USA; Cambridge, MA, USA; Addison, TX, USA; Los Angeles, CA, USA; Miami, FL, USA; Mountain View, CA, USA; Reston, VA, USA; Seattle, WA, USA; San Francisco, CA, USA; Sunnyvale, CA, USA; Washington D.C., DC, USA.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.

This role may also be located in our Playa Vista, CA campus.

Note: By applying to this position you will have an opportunity to share your preferred working location from the following:.
Minimum qualifications:
  • Bachelor's degree in Computer Science or equivalent practical experience.
  • 8 years of experience building machine learning solutions and working with technical customers.
  • Experience designing cloud enterprise solutions and supporting customer projects to completion.
  • Experience coding in one or more general purpose languages (e.g., Python, Java, Go, C or C++) including data structures, algorithms, and software design.

Preferred qualifications:
  • Experience working with recommendation engines, data pipelines, or distributed machine learning.
  • Experience with deep learning frameworks (e.g., Tensorflow, pyTorch, XGBoost).
  • Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools and environments (e.g., Apache Beam, Hadoop, Spark, Pig, Hive, MapReduce, Flume).
  • Understanding of the auxiliary practical concerns in production machine learning systems.

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