Cloud Data and AI Engineer, Professional Services

As a Cloud Data and AI Engineer, you will guide Public Sector customers to develop, configure and deploy their data and AI solutions. Together with the team, you will support customer implementations of Google Cloud products through architecture guidance, best practices, data migration, capacity planning, implementation, troubleshooting, monitoring, and more. You will consult with customers on how to best design their data and AI solutions including development and deployment of ML models, and integrations with leading Google technologies. You will 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 drive excellence in Google Cloud products and features.Google Public Sector brings the magic of Google to the mission of government and education with solutions purpose-built for enterprises. We focus on helping United States public sector institutions accelerate their digital transformations, and we continue to make significant investments and grow our team to meet the complex needs of local, state and federal government and educational institutions.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $127000 - $183000 (USD) + 15% bonus target + bonus + equity + benefits

Learn more about benefits at Google.

Minimum qualifications:

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 3 years of experience with software development in Python, Java, or C++, and relational database technologies.
• Experience implementing data and AI solutions (including Large Language Models (LLMs)) and providing technical leadership to business stakeholders and education to partners.
• Ability to travel up to 30% of the time as needed.
• Must possess an active Secret security clearance.

Preferred qualifications:

• Experience with database and AI integrations.
• Experience with machine learning operations, data warehousing, and data pipeline development, including ETL and ELT.
• Experience working with cloud databases such as RDS, Aurora, ElastiCache, CloudSQL, AlloyDB, Datastore, or Bigtable.
• Experience in database administration techniques including storage, clustering, availability, disaster recovery, security, logging, performance tuning, monitoring and auditing.
• Experience developing, deploying, and managing machine learning models, including experience writing software in one or more languages, such as Java, Python, or Golang.
• Experience with database management tools for backups, recovery, snapshot management, sharding, partitioning and as well as database performance tuning.