Snowflake AI/ML Sr Solutions Architect

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.