Senior Solutions Architect helping customers design, build, and productionize AI/ML pipelines on Snowflake Data Cloud. Requires 5+ years customer-facing technical experience, deep knowledge of generative AI and ML lifecycles, MLOps, and hands-on Python/SQL skills.
160k – 210k
Remote5+ YOESolutions Architecture
About the role
Responsibilities
Serve as a technical expert on Snowflake in relation to AI/ML workloads, providing customers with best practices.
Work with customers to understand AI/ML use cases, discover key requirements, and architect Snowflake-centric solutions.
Build, deploy, and optimize AI and ML pipelines using Snowflake features and ecosystem partners.
Hands-on development using SQL, Python, and Cortex AI to build POCs demonstrating implementation techniques and best practices.
Ensure knowledge transfer to enable customers to extend Snowflake capabilities independently.
Maintain deep understanding of competitive and complementary AI/ML technologies and position Snowflake effectively.
Provide guidance on resolving customer-specific technical challenges.
Support Services Delivery team members in developing expertise.
Collaborate with Product Management, Engineering, and Marketing to improve Snowflake’s products and marketing.
Requirements
Minimum 5 years experience working with customers in a pre-sales or post-sales technical role.
Outstanding presentation skills to technical and executive audiences, including whiteboarding, presentations, and demos.
Thorough understanding of generative AI and agent lifecycles: document ingestion, vector embedding selection, LLM selection and optimization, genAI monitoring and evaluation.
Thorough understanding of the complete ML lifecycle: feature engineering, model development, model deployment, and model management.
Strong understanding of AI/MLOps, including technologies and methodologies for deploying and monitoring models and agents.
Experience with at least one public cloud platform (AWS, Azure, or GCP).
Experience with at least one AI/ML platform such as AWS SageMaker, Databricks, GCP Vertex AI, Azure ML, Dataiku, or DataRobot.
Hands-on scripting experience with SQL and at least one of Python, Java, or Scala.
Experience with libraries such as Pandas, PyTorch, TensorFlow, Scikit-Learn, LangChain/LangGraph, LlamaIndex, or similar.
University degree in data science, computer science, engineering, mathematics, or related fields (or equivalent experience).
Nice-to-Haves
Experience with Databricks/Apache Spark.
Experience implementing data pipelines using ETL tools.
Proven success at enterprise software companies.
Vertical expertise in FSI, Retail, Manufacturing, or similar.
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