Cloud Engineer, AI/ML, Professional Services, Google Cloud

$171,000 - $257,000/Yr

Google - Atlanta, GA

Full-time - Mid Level

Atlanta, GA

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About the position

The Cloud AI Engineer role at Google Cloud involves designing and implementing machine learning solutions for various customer use cases. This position is part of the Google Cloud Consulting Professional Services team, which guides customers through their cloud journey, helping them leverage Google's technology to transform their businesses. The role requires collaboration with customers to identify machine learning opportunities, deployment of solutions, and delivery of educational workshops. Additionally, the engineer will work closely with product management and engineering teams to enhance Google Cloud products.

Responsibilities

• Be a trusted technical advisor to customers and solve complex machine learning issues.
• Coach customers on practical issues in machine learning systems such as 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% of the time in the region for meetings, technical reviews, and onsite delivery activities as needed.

Requirements

• Bachelor's degree in Computer Science or equivalent practical experience.
• 8 years of experience designing cloud enterprise solutions and supporting customer projects to completion.
• 8 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.

Nice-to-haves

• 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 auxiliary practical concerns in production machine learning systems.

Benefits

• Bonus
• Equity
• Health insurance
• Retirement savings plan (401k)
• Paid time off (PTO)