Senior Solution Engineer

Join Snowflake as a Senior Solution Engineer in Chicago. In this role, you will solve complex customer problems, drive large deal closures, and work directly with sales teams and channel partners. You will present Snowflake's technology and vision, collaborate with various teams, and support enterprise Proof of Concepts and implementation designs. The ideal candidate will have a strong background in computer science, engineering, or mathematics, and experience in technical presales or solutions engineering.

• Collaborate with sales teams and channel partners to understand customer needs and provide compelling demonstrations of Snowflake technology.
• Drive strategic solutions to close business opportunities by designing and demonstrating solutions leveraging LLMs, retrieval-augmented generation (RAG), vector search, and AI functions within Snowflake.
• Support enterprise Proof of Concepts and implementation designs, including end-to-end ML pipelines and GenAI applications built on Snowflake.
• The ideal candidate will be passionate about reinventing the database space — including how AI and ML are transforming it — and comfortable engaging with both executive and technical audiences
• University degree in computer science, engineering, mathematics, or equivalent experience
• Strong customer-facing communication skills
• Ability to connect business problems with technical solutions, including AI/ML-driven approaches
• Working knowledge of LLMs, generative AI, prompt engineering, and embedding-based search
• Broad experience with Database, Data Warehouse, ETL, and cloud technologies
• Familiarity with machine learning concepts and frameworks (e.g., scikit-learn, PyTorch, TensorFlow, or similar)
• Hands-on expertise with SQL and Python
• Outstanding presentation skills for both technical and executive audiences
• Experience with Snowflake Cortex AI, Snowpark ML, or Snowflake's Model Registry
• Experience building data pipelines using open table formats (such as Apache Iceberg or Delta Lake) and managing modern data catalogs (e.g., Unity Catalog, Polaris, Dremio, or AWS Glue Catalog)
• Familiarity with MLOps practices — model deployment, monitoring, and retraining pipelines
• Experience building or deploying RAG architectures, agentic workflows, or multi-modal AI applications
• Exposure to vector databases or semantic search tooling (e.g., Pinecone, Weaviate, pgvector)
• Background in a technical presales or solutions engineering role at an AI/ML or data platform company