Sales Solution Architect – AI/ML

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Snowflake

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

Founded 2012

☁️ SaaS

Cloud Computing • Data Analytics • SaaS

Snowflake is a cloud-based data-warehousing company that provides a platform for data storage, processing, and analytics. It allows businesses to store data in a centralized location and perform complex queries and analytics on that data efficiently. Snowflake is designed to handle a wide range of data workloads and can scale dynamically to meet the needs of growing businesses.

📋 Description

• Be a technical expert on all aspects of Snowflake in relation to the AI/ML workload • Create and propose solutions to solve customer use cases, 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, and APIs to build POCs that demonstrate implementation techniques and best practices on Snowflake technology for GenAI and ML workloads • 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 • Maintain deep understanding of competitive and complementary technologies and vendors within the AI/ML space, and how to position Snowflake in relation to them • Work with System Integrator consultants at a deep technical level to successfully position and deploy Snowflake in customer environments • Provide guidance on how to resolve customer-specific technical challenges • Support other members of the Services Delivery team develop their expertise • Collaborate with Product Management, Engineering, and Marketing to continuously improve Snowflake’s products and marketing

🎯 Requirements

• Minimum 10 years experience working with customers in a pre-sales or post-sales technical role • Skills presenting to both technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos • Thorough understanding of the complete Data Science life-cycle including feature engineering, model development, model deployment and model management. • Strong understanding of MLOps, coupled with technologies and methodologies for deploying and monitoring models • Experience and understanding of at least one public cloud platform (AWS, Azure or GCP) • Experience with at least one Data Science tool such as Sagemaker, AzureML, Vertex, Dataiku, DataRobot, H2O, and Jupyter Notebook

🏖️ Benefits

• Ability and flexibility to travel to work with customers on-site 25% of the time