Cloud Engineer II, AI/ML, Professional Services

4 Months ago • 6 Years +

Job Description

The Cloud Engineer II, AI/ML, Professional Services role involves designing and implementing machine learning solutions for customer use cases, leveraging Google products like TensorFlow, DataFlow, and Vertex AI. Responsibilities include working with customers to identify ML opportunities, deploying solutions, delivering workshops, and collaborating with Product Management and Engineering. This role requires strong communication skills in English and Spanish, solving complex ML issues, coaching customers on practical aspects of ML systems, and delivering tailored solutions. Up to 30% travel is expected for meetings and onsite delivery. The position requires expertise in building machine learning solutions, cloud enterprise solutions, and experience working with technical clients.
Good To Have:
  • Recommendation engines
  • Data pipelines/distributed ML
  • TensorFlow/PyTorch/XGBoost
  • Data warehousing knowledge
  • Apache Beam/Hadoop/Spark
Must Have:
  • Bachelor's degree in CS or equivalent
  • 6+ years building ML solutions
  • Experience with Python, Java, Go, C/C++
  • Cloud enterprise solution design
  • Fluent English & Spanish
  • Customer project support

Add these skills to join the top 1% applicants for this job

java
tensorflow
algorithms
spark
deep-learning
apache-beam
data-structures
python
pytorch
hadoop
communication
problem-solving


Minimum qualifications:

  • Bachelor's degree in Computer Science or equivalent practical experience.
  • 6 years of experience building machine learning solutions and working with technical customers.
  • Experience coding in one or more general purpose languages (e.g., Python, Java, Go, C or C++) including data structures, algorithms, and software design.
  • Experience designing cloud enterprise solutions and supporting customer projects to completion.
  • Ability to communicate in English and Spanish fluently to support client relationship management in this region.

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, Flume).
  • Understanding of the auxiliary practical concerns in production machine learning systems.

About the job

The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google’s global network, web-scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.

In this role, you will design and implement machine learning solutions for customer use cases, leveraging Google products including TensorFlow, DataFlow, and Vertex AI. You will work with customers to identify opportunities to apply machine learning in their business, and travel to customer sites to deploy solutions and deliver workshops to educate and empower customers. Additionally, you will work closely with Product Management and Product Engineering to build and constantly drive excellence in our products.

You are the Google Engineer working with Google's most ambitious Cloud customers. Together with the team you will support customer implementation of Google Cloud products through: architecture guidance, best practices, data migration, capacity planning, implementation, troubleshooting, monitoring, etc.

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

  • Be a trusted technical advisor to customers and solve complex machine learning issues.
  • Coach customers on the practical tests in machine learning systems like feature extraction and feature definition, data validation, monitoring, and management of features and models.
  • Work with Customers, Partners, and Google Product teams to deliver tailored solutions into production.
  • Create and deliver best practice recommendations, tutorials, blog articles, and sample code.
  • Travel up to 30% for in-region for meetings, technical reviews, and onsite delivery activities.

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