Cloud Data and AI Engineer, Professional Services

Google Google · Big Tech · Reston, VA +1

This role focuses on guiding Public Sector customers in developing, configuring, and deploying data and AI solutions on Google Cloud Platform. It involves providing architecture guidance, best practices, data migration, and troubleshooting for ML models and integrations. The engineer will also consult on optimal data and AI solution design and travel to customer sites for deployment and workshops.

What you'd actually do

  1. Be highly collaborative and work closely with data producers, consumers and Data and AI Engineering teams across public sector customers and teams to understand the data needs, provide consultation, and design and develop solutions.
  2. Analyze on-premise and cloud database environments, consulting on the optimal design for performance and deployment on Google Cloud Platform.
  3. Create and deliver best practices recommendations, tutorials, blog articles, sample code, and technical presentations, adapting to different levels of key business and technical stakeholders.
  4. Translate business requirements into conceptual, logical, and physical data models.
  5. Design, build, and maintain data and AI solutions.

Skills

Required

  • 3 years of experience with software development in Python, Java, or C++
  • relational database technologies
  • Experience implementing data and AI solutions (including Large Language Models (LLMs))
  • providing technical leadership to business stakeholders
  • education to partners
  • Ability to travel up to 30% of the time as needed
  • Secret security clearance

Nice to have

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

What the JD emphasized

  • Must possess an active Secret security clearance.

Other signals

  • customer-facing
  • deployment
  • architecture guidance
  • ML models
  • Google Cloud Platform