Senior / Principal Technical Architect, Ai/ml

Snowflake Snowflake · Data AI · Pune, IN · Professional Services

This role is for a Senior/Principal Technical Architect on the Professional Services team at Snowflake. The primary focus is on helping customers implement and deploy Data Science and AI/ML workloads on the Snowflake Data Cloud. This involves advising clients on best practices, designing solutions, building and deploying ML pipelines, and providing hands-on support using SQL and Python. The role requires a strong understanding of the complete Data Science lifecycle, MLOps, and cloud platforms, with a focus on enabling customers to leverage Snowflake's capabilities for their AI/ML initiatives.

What you'd actually do

  1. Be a technical expert on all aspects of Snowflake in relation to the AI/ML workload
  2. Provide customers with best practices and advise as it relates to Data Science workloads on Snowflake
  3. Build, deploy and ML pipelines using Snowflake features and/or Snowflake ecosystem partner tools based on customer requirements
  4. 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
  5. 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

Skills

Required

  • Data science
  • Cloud architecture
  • Snowflake
  • SQL
  • Python
  • Data Science life-cycle
  • MLOps
  • Public cloud platform (AWS, Azure or GCP)
  • Data Science tools (AWS Sagemaker, AzureML, Dataiku, Datarobot, H2O, Jupyter Notebooks)
  • Pandas
  • PyTorch
  • TensorFlow
  • SciKit-Learn

Nice to have

  • GenerativeAI
  • LLMs
  • Vector Databases
  • Databricks/Apache Spark
  • ETL tools
  • Data Science role experience
  • Enterprise software experience
  • Vertical expertise

What the JD emphasized

  • Minimum 6 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

Other signals

  • AI/ML workload
  • Data Science pipelines
  • MLOps
  • model deployment
  • model management