Senior Solutions Architect - Ai/ml - Services Delivery

Snowflake Snowflake · Data AI · NC, United States · Remote · Professional Services

Senior Solutions Architect role focused on helping customers leverage Snowflake's Data Cloud for AI/ML workloads, from ideation to production. The role involves designing tailored AI/ML solutions, advising on best practices, and working hands-on with SQL, Python, and Cortex AI features to build POCs. It requires a strong understanding of generative AI and ML lifecycles, MLOps, and cloud platforms.

What you'd actually do

  1. Be a technical expert on all aspects of Snowflake in relation to the AI/ML workload and provide customers with best practices given Snowflakes technology stack.
  2. Work with customers to understand their AI/ML use case, discover key requirements, and architect a Snowflake-centric solution to be delivered by Services Delivery.
  3. Understand how to build, deploy and AI and ML pipelines using Snowflake features and/or Snowflake ecosystem based on customer requirements.
  4. Work hands-on where needed using SQL, Python, and Cortex AI features to build POCs that demonstrate implementation techniques and best practices on Snowflake technology within the AI/ML 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

  • Snowflake Data Cloud
  • AI/ML workload expertise
  • Customer-facing technical consulting
  • Designing AI/ML solutions
  • Building and deploying AI/ML pipelines
  • SQL
  • Python
  • Cortex AI features
  • Generative AI lifecycles
  • Agent lifecycles
  • Document ingestion
  • Vector embedding selection
  • LLM selection and optimization
  • GenAI monitoring and evaluation techniques
  • Complete ML life-cycle
  • Feature engineering
  • Model development
  • Model deployment
  • Model management
  • AI/MLOps
  • Model and agent deployment and monitoring
  • Public cloud platform (AWS, Azure or GCP)
  • AI/ML platform (e.g., AWS Sagemaker, Databricks, GCP Vertex AI, AzureML, Dataiku, Datarobot)
  • Scripting experience with SQL and Python, Java or Scala
  • Libraries such as Pandas, PyTorch, TensorFlow, SciKit-Learn, LangChain/LangGraph, LlamaIndex or similar

Nice to have

  • Databricks/Apache Spark
  • ETL tools
  • Enterprise software implementation
  • Vertical expertise (FSI, Retail, Manufacturing)

What the JD emphasized

  • Minimum 5 years experience working with customers in a pre-sales or post-sales technical role.
  • Thorough understanding of the common generative AI and agent lifecycles including document ingestion, vector embedding selection, llm selection and optimization, genAI monitoring and evaluation techniques.
  • Thorough understanding of the complete ML life-cycle including feature engineering, model development, model deployment and model management.
  • Strong understanding of AI/MLOps, coupled with technologies and methodologies for deploying and monitoring models and agents.

Other signals

  • customer-facing technical expert
  • designing and implementing AI/ML solutions
  • Snowflake Data Cloud
  • production-ready AI/ML workloads
  • MLOps