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Job Application for Solutions Architect, AI/ML

Snowflake

San Francisco, CAOn-siteFull-time$140k – $220k / year

About this role

Job Application for Solutions Architect, AI/ML at Snowflake Solutions Architect, AI/ML at Snowflake(View all jobs) Build the future of data. Join the Snowflake team. We are looking for people who have a strong background in data science and cloud architecture to join our Professional Services team to help create exciting new offerings and capabilities for our customers! This team will be working with customers using Snowflake to expand their use of the Snowflake Data Cloud to bring data science pipelines from ideation to deployment, and beyond using Snowflake's features and its extensive partner ecosystem. The role will be more strategic, advising our clients on best practices and advice to implement Data Science workloads on Snowflake. You will be designing solutions based on requirements and coordinating with customer teams, and where needed Systems Integrators, while maintaining oversight and direction to ensure successful outcomes AS A SOLUTIONS ARCHITECT - AI/ML AT SNOWFLAKE, YOU WILL : - Collaborate with Product Management, Engineering, and Marketing to continuously improve Snowflake’s products and marketing. - Support other members of the Professional Services team develop their expertise. - Provide guidance on how to resolve customer-specific technical challenges. - Work with System Integrator consultants at a deep technical level to successfully position and deploy Snowflake in customer environments - Maintain deep understanding of competitive and complementary technologies and vendors within the AI/ML space, and how to position Snowflake in relation to them - Follow best practices, including ensuring knowledge transfer so that customers are properly enabled and are able to extend the capabilities of Snowflake on their own - Work hands-on where needed using SQL, Python, Java and/or Scala to build POCs that demonstrate implementation techniques and best practices on Snowflake technology within the Data Science workload. - Build, deploy and ML pip

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