Senior Ai/ml Architect, Applied Field Engineering

Snowflake Snowflake · Data AI · NY-New York, United States · Solution Engineering

Senior AI/ML Architect, Applied Field Engineering role at Snowflake, focusing on architecting and positioning AI solutions on the Snowflake AI Data Cloud for enterprise customers. Responsibilities include technical expertise, driving POCs, influencing roadmaps, publishing content, and maintaining sales engineering assets. Requires experience in building and deploying ML/GenAI solutions, familiarity with RAG, prompt engineering, fine-tuning, Python, ML packages, and data engineering tools.

What you'd actually do

  1. Be the technical expert in the room that positions Snowflake’s AI and ML features and value to technical stakeholders at Snowflake’s customers across the Americas.
  2. Partner with Snowflake account team teams and customer champions to scope and drive POCs to success and technical wins that prove the value of Snowflake’s capabilities, including executive readouts and business value cases.
  3. Collaborate with Snowflake’s product and engineering teams to influence Snowflake’s AI and ML roadmaps based on customer feedback.
  4. Publish content that helps the team and company scale beyond your individual efforts, like blog posts, presentations at conferences, or technical collateral like notebooks and demos.
  5. Influence, tailor and maintain Sales Engineering AI and ML selling assets, including customer presentations, demonstrations, and customer stories.

Skills

Required

  • 5+ years of experience building and deploying machine learning and generative AI solutions in the cloud
  • Familiarity and associated knowledge of generative AI techniques like RAG, few shot learning, prompt engineering, or fine-tuning
  • Deep knowledge of Python and common ML packages (such as LangChain, pandas, sklearn, and PyTorch)
  • Deep knowledge of data engineering tools and technologies like dbt, Airflow, and Spark
  • Strong presentation skills to both technical and executive audiences

Nice to have

  • Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages
  • Experience and understanding of large-scale infrastructure-as-a-service platforms (e.g. AWS, Microsoft Azure, GCP, etc.)
  • 1+ years of practical Snowflake experience
  • Knowledge of and experience with large-scale database technology (e.g. Snowflake, Netezza, Exadata, Teradata, Greenplum, etc.)

What the JD emphasized

  • building and deploying machine learning and generative AI solutions in the cloud
  • generative AI techniques like RAG, few shot learning, prompt engineering, or fine-tuning
  • operationalize enterprise AI use cases like interactive chat applications or text processing

Other signals

  • customer adoption
  • technical wins
  • customer feedback
  • AI solutions