Senior Ai/ml Architect, Applied Field Engineering/field Cto

Snowflake Snowflake · Data AI · Singapore · Solution Engineering

Snowflake is seeking an AI Specialist for their Sales Engineering team to provide hands-on expertise and support for AI solutions built on the Snowflake AI Data Cloud. The role involves working with technical decision makers and data scientists, scoping and driving POCs, collaborating with product and engineering teams, and publishing content to scale AI/ML adoption.

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 APJ region.
  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

  • 10+ years of experience building and deploying machine learning
  • 3+ years of building and deploying Generative AI solutions in the cloud
  • Practitioner level hands-on experience on Machine Learning workload including MLOps, Feature Store, Model Deployment, Model Explainability and Observability
  • 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)
  • 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.)
  • Working experience with quota carrying roles in the past.

What the JD emphasized

  • building and deploying Generative AI solutions
  • hands-on experience on Machine Learning workload including MLOps, Feature Store, Model Deployment, Model Explainability and Observability
  • 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) as well as data engineering tools and technologies like dbt, Airflow, and Spark.

Other signals

  • AI Data Cloud
  • Generative AI solutions
  • customer adoption of AI/ML use cases