What are the responsibilities and job description for the Snowflake AI/ML Sr Solutions Architect position at ExecutivePlacements.com?
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
- 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 GCP).
- 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.
