Sales Solution Architect Ai/ml

Snowflake Snowflake · Data AI · Singapore · Professional Services

This role is a Sales Solution Architect focused on AI/ML workloads at Snowflake. The primary responsibility is to lead the pre-sales motion, understanding customer business problems, designing solutions leveraging Snowflake, and guiding customers from concept to deployment. It involves technical expertise, POC development, and collaboration with various internal teams and external partners.

What you'd actually do

  1. Be a technical expert on all aspects of Snowflake in relation to the AI/ML workload
  2. 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
  3. 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
  4. 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
  5. Maintain deep understanding of competitive and complementary technologies and vendors within the AI/ML space, and how to position Snowflake in relation to them

Skills

Required

  • Pre-sales technical role experience
  • Data Science life-cycle understanding
  • MLOps understanding
  • Public cloud platform experience (AWS, Azure or GCP)
  • Data Science tool experience (Sagemaker, AzureML, Vertex, Dataiku, DataRobot, H2O, Jupyter Notebook)
  • Technical and executive audience presentation skills
  • SQL
  • Python
  • APIs

Nice to have

  • Knowledge transfer
  • System Integrator collaboration

What the JD emphasized

  • Minimum 10 years experience working with customers in a pre-sales or post-sales technical role
  • 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