Technical Architect Ai/ml

Snowflake Snowflake · Data AI · Victoria, Australia · Remote · Professional Services

Snowflake is seeking a Technical Architect for their AI/ML Workload Services team. This role involves designing and deploying AI/ML pipelines using Snowflake features and ecosystem tools, working hands-on with SQL and Python to build POCs, and enabling customers. The architect will also maintain knowledge of competitive technologies and collaborate with internal teams.

What you'd actually do

  1. Be a technical expert on all aspects of Snowflake in relation to the AI/ML workload
  2. Build, deploy and ML pipelines using Snowflake features and/or Snowflake ecosystem partner tools based on customer requirements
  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

  • Snowflake
  • SQL
  • Python
  • Data Science life-cycle
  • MLOps
  • Public cloud platform (AWS, Azure or GCP)
  • Data Science tools (Sagemaker, AzureML, Vertex, Dataiku, DataRobot, H2O, Jupyter Notebooks)
  • Large Language Models
  • Retrieval frameworks
  • Agentic frameworks
  • Pandas
  • PyTorch
  • TensorFlow
  • SciKit-Learn

Nice to have

  • Generative AI
  • LLMs
  • Vector Databases
  • Databricks/Apache Spark
  • PySpark
  • ETL tools
  • Data Science role
  • enterprise software
  • Vertical expertise

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
  • Experience with Large Language Models, Retrieval and Agentic frameworks

Other signals

  • designing solutions
  • customer requirements
  • POCs
  • MLOps
  • LLMs
  • Agentic frameworks