1 month ago

Technical Solutions Engineer, Cloud AI

Google

On Site
Full Time
$184,000
San Francisco, CA
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Job Overview

Job TitleTechnical Solutions Engineer, Cloud AI
Job TypeFull Time
Offered Salary$184,000
LocationSan Francisco, CA

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Job Description

About The Job

The Google Cloud Platform team helps customers transform and build what's next for their business — all with technology built in the cloud. Our products are developed for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers — developers, small and large businesses, educational institutions and government agencies — see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners. As a Technical Solutions Engineer, you will own important customer issues and provide level two support to other support teams. In this role, you will be part of a global team that provides 24x7 support to help customers seamlessly make the switch to Google Cloud. You will troubleshoot technical problems for customers using a mix of debugging, networking, system administration, updating documentation, and when needed, coding or scripting. You will make products easier to adopt and use by making improvements to the product, tools, processes, and documentation. As the Technical Solutions team is driven by customers, you will help drive the success of Google Cloud by understanding and advocating for customers’ issues. 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.

Responsibilities

  • Collaborate with customers on ML deployments to resolve issues and achieve production, availability, and scale while partnering with product and engineering teams to improve products based on customer feedback.
  • Manage customer problems through effective diagnosis, resolution, documentation, or implementation of investigation tools to increase productivity for customer issues on Google Cloud Platform products.
  • Develop an understanding of Google Cloud’s AI and ML products or solutions and underlying architectures by troubleshooting, reproducing, and determining the root cause for customer-reported issues and building tools for faster diagnosis.
  • Serve as a consultant and SME for internal stakeholders in engineering, sales, and customer organizations to resolve technical deployment obstacles and improve Google Cloud.
  • Work as part of a team of engineers or consultants that ensure 24-hour customer support, including working non-standard work hours or shifts and possibly including weekends.

Minimum Qualifications

  • Bachelor’s degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
  • 6 years of experience with two or more of the following: web technology, data/big data, systems administration, machine learning, networking, kubernetes.
  • Experience coding in one or more general purpose languages (e.g., Python, Java, Go, C, or C++) including data structures and algorithms, and software design.
  • Experience with AI model training, performance analysis, and integration with other cloud services supporting customer projects to completion.
  • Experience in computer networking (e.g., firewalls, routing, load balancing, etc.), web technologies (e.g., HTTP, HTML, DNS, TCP, etc.), and AI concepts and techniques.

Preferred Qualifications

  • 9 years of experience in recommendation systems, natural language processing, speech recognition, or computer vision.
  • Experience troubleshooting ML models (e.g., TensorFlow, Keras, PyTorch).
  • Experience working with public cloud services and infrastructure, AI architecture, and networking/peering with private cloud.
  • Knowledge of data warehousing concepts, data warehouse technical architectures, infrastructure components, ETL/ELT, and reporting/analytic tools and environments (e.g., Apache Beam, Hadoop, Spark, etc.).
  • Ability to recommend ML best practices for practical business use.
  • Ability to lead the design and implementation of AI-based solutions, web services, debugging tools with effective leadership and influencing skills in AI/ML application.

Equal Opportunity Statement

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.

Key skills/competency

  • Technical Solutions Engineer
  • Cloud AI
  • Google Cloud Platform
  • Machine Learning
  • Deep Learning
  • Python
  • Kubernetes
  • Networking
  • System Administration
  • Web Technologies

Tags:

Technical Solutions Engineer
Cloud AI
Google Cloud
Machine Learning
Deep Learning
Python
Kubernetes
Networking
System Administration
Web Technologies
AI
ML
Cloud Computing
Troubleshooting
Customer Support
Software Development
Data Structures
Algorithms
Software Design
AI Model Training
Performance Analysis
Cloud Services
Computer Networking
Web Technologies
HTTP
HTML
DNS
TCP
AI Concepts
Recommendation Systems
Natural Language Processing
Speech Recognition
Computer Vision
TensorFlow
Keras
PyTorch
Public Cloud
AI Architecture
Data Warehousing
ETL
ELT
Apache Beam
Hadoop
Spark
ML Best Practices
AI Solutions
Web Services
Debugging Tools
Leadership Skills
Influencing Skills
Customer Issues
Production Availability
Scale
Product Improvement
Root Cause Analysis
Internal Stakeholders
Engineering
Sales
Customer Organizations
Technical Deployment
24-hour Customer Support
Non-standard Work Hours
Bachelor's Degree
Science
Technology
Engineering
Mathematics
Equivalent Practical Experience
Web Technology
Data Big Data
Systems Administration
Machine Learning
Networking
Kubernetes
Coding
General Purpose Languages
Python
Java
Go
C
C++
Data Structures
Algorithms
Software Design
AI Model Training
Performance Analysis
Integration
Cloud Services
Customer Projects
Completion
Computer Networking
Firewalls
Routing
Load Balancing
Web Technologies
HTTP
HTML
DNS
TCP
AI Concepts
Techniques
Recommendation Systems
Natural Language Processing
Speech Recognition
Computer Vision
Troubleshooting ML Models
TensorFlow
Keras
PyTorch
Public Cloud Services
Infrastructure
AI Architecture
Networking Peering
Private Cloud
Data Warehousing Concepts
Data Warehouse Technical Architectures
Infrastructure Components
ETL
ELT
Reporting Analytic Tools
Environments
Apache Beam
Hadoop
Spark
ML Best Practices
Practical Business Use
Design Implementation
AI-based Solutions
Web Services
Debugging Tools
Leadership Skills
Influencing Skills
AI ML Application
US Base Salary
Full-time
Bonus
Equity
Benefits
Work Location
Recruiter
Hiring Process
Compensation Details
Base Salary
Google
Equal Opportunity Workplace
Affirmative Action Employer
Equal Employment Opportunity
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Sex
National Origin
Sexual Orientation
Age
Citizenship
Marital Status
Disability
Gender Identity
Veteran Status
Criminal Histories
Legal Requirements
EEO Policy
EEO is the Law
Disability Special Need
Accommodation
Accommodations for Applicants
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How to Get Hired at Google

  • Tailor your resume: Highlight AI/ML, cloud, coding, and networking experience.
  • Showcase problem-solving: Detail your troubleshooting and customer support achievements.
  • Demonstrate technical depth: Emphasize your experience with ML models and cloud infrastructure.
  • Prepare for technical interviews: Brush up on data structures, algorithms, and AI/ML concepts.
  • Research Google Cloud: Understand their AI offerings and customer success stories.

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