Technical Solutions Consultant

Google · Addison, TX, USA

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.


The US base salary range for this full-time position is $183,000 - $265,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.


Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.


Position reports to the Google Addison, TX office & may allow for a hybrid schedule as per Google policy.


Minimum qualifications:

  • Bachelor’s degree in Computer Science, Engineering, Mathematics or a related field and 7 years of progressive, post-baccalaureate experience in the job offered or in a Technical Solutions Consultant-related occupation.
  • Alternatively, will accept a Master’s degree in Computer Science, Engineering, Mathematics or a related field and 5 years of experience in the job offered or in a Technical Solutions Consultant-related occupation.
  • Position requires 5 years of experience in the following:

    • System architecture design using distributed patterns for cloud solutions

    • Container orchestration with Kubernetes including GKE or EKS

    • DevOps practices for system automation or CI/CD pipeline management

    • Model deployment and fine-tuning using python and machine learning libraries such as PyTorch, TensorFlow, Scikit-learn, or Keras; and

    • Customer-facing migration planning and execution or enterprise cloud implementation.

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