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Snowflake AI/ML Sr Solutions Architect Remote JOB DESCRIPTION • Be a technical expert on all aspects of Snowflake in relation to the AI/ML workload. • Provide customers with best practices and advise as it relates to Data Science workloads on Snowflake • Build and deploy ML pipelines using Snowflake features and/or Snowflake ecosystem partner tools based on customer requirements. • Work hands-on where needed using SQL, Python, to build POCs that demonstrate implementation techniques and best practices on Snowflake technology within the Data Science workload. • 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 Professional Services team develop their expertise. • Collaborate with Product Management, Engineering, and Marketing to continuously improve Snowflake s products and marketing. • Replatform customer chat bot to Snowflake Cortex Analyst, add Cortex Search support for unstructured data types Discover and integrate existing Churn Prediction model into new chat bot REQUIREMENTS • University degree in data science, computer science, engineering, mathematics or related fields, or equivalent experience. • 12-14 years experience working with customers in a pre-sales or post-sales technical role. • Outstanding 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 Google Cloud Platform). • Experience with at least one Data Science tool such as AWS Sagemaker, AzureML, Dataiku, Datarobot, H2O, and Jupyter Notebooks. • Hands-on scripting experience with SQL and at least one of the following; Python, Java or Scala. • Experience with libraries such as Pandas, PyTorch, TensorFlow, SciKit-Learn or similar. • Experience with GenerativeAI, LLMs and Vector Databases. • Experience with Databricks/Apache Spark. • Experience implementing data pipelines using ETL tools. • Experience working in a Data Science role. • Proven success at enterprise software. • Vertical expertise in a core vertical such as FSI, Retail, Manufacturing, etc.