Skills

Must have

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.

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.
GenAI - Cortex Search / RAGREQUIREMENTS

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.

Snowflake AI/ML Sr Solutions Architect

Compensation

Not disclosed

Location

York, USA

Work setup

Onsite

Seniority

Senior

Employment

Not specified

Experience

12+ years

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.

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.
GenAI - Cortex Search / RAGREQUIREMENTS

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.