Principal Solutions Architect - AI/ML - Services Delivery

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. We are seeking experienced professionals with a strong background in Artificial Intelligence, Machine Learning, and Cloud Architecture to join our Services Delivery team to help create exciting new offerings and capabilities for our customers! In this strategic role, you will help customers expand their use of the Snowflake Data Cloud to bring AI/ML pipelines from ideation to full production. Leveraging Snowflake’s native features and extensive partner ecosystem, you will advise clients on best practices for scaling production-ready workloads. You will design tailored AI/ML solutions, coordinate closely with customer teams and Systems Integrators, and provide the technical leadership and oversight needed to ensure successful outcomes. AS A SR. SOLUTIONS ARCHITECT AT SNOWFLAKE, YOU WILL: - 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.

• 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.
• Understand how to build, deploy and AI and ML pipelines using Snowflake features and/or Snowflake ecosystem based on customer requirements.
• 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.
• 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 - Maintain deep understanding of competitive and complementary technologies and vendors within the AI/ML space, and how to position Snowflake in relation to them - Provide guidance on how to resolve customer-specific technical challenges.
• Support other members of the Services Delivery team develop their expertise.
• Collaborate with Product Management, Engineering, and Marketing to continuously improve Snowflake’s products and marketing.

• Outstanding skills presenting to both technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos - 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.
• Experience and understanding of at least one public cloud platform (AWS, Azure or GCP) - Experience with at least one AI/ML platform such as AWS Sagemaker, Databricks, GCP and Vertex AI, AzureML, Dataiku, Datarobot, etc.
• Hands-on scripting experience with SQL and at least one of the following; 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 BONUS POINTS FOR HAVING: - Experience with Databricks/Apache Spark - Experience implementing data pipelines using ETL tools - Proven success at enterprise software - Vertical expertise in a core vertical such as FSI, Retail, Manufacturing etc.

• Highlight hands‑on experience with Snowflake’s platform (e.g., building data pipelines with Snowpipe, using Snowpark for Python/Scala, and optimizing queries with Snowflake’s automatic clustering). In your resume and interview, cite specific projects where you integrated AI/ML models directly into Snowflake tables or materialized views, quantifying performance gains or cost savings.
• Prepare a concise “AI‑native solution showcase” (5‑7 minute video or interactive notebook) that demonstrates end‑to‑end workflow: ingest raw data into Snowflake, transform with Snowpark, train a model using Snowflake’s native ML capabilities (e.g., Snowflake Native Apps, Snowpark ML), and deploy the model as a Snowflake Secure Function or external service. Share the link in your application and be ready to walk interviewers through the architecture and trade‑offs.
• Emphasize low‑ego, experimental mindset by describing at least two “rapid‑prototype” experiments you ran on emerging Snowflake or generative‑AI features (e.g., LLM‑driven data enrichment, Vector Search). Explain hypothesis, metrics, iteration speed, and how you pivoted or scaled the solution—this aligns with Snowflake’s culture of testing and simplifying.
• Anticipate scenario‑based interview questions that focus on service delivery: be ready to map a client’s business problem to a Snowflake‑centric AI solution, outline governance (role‑based access, data masking), SLAs, and cost‑optimization strategies (e.g., using Snowflake’s auto‑scaling warehouses, resource monitors). Practice articulating the end‑to‑end delivery timeline and how you’d collaborate with cross‑functional teams remotely.
• When negotiating salary, reference Snowflake’s market‑leading total‑compensation benchmarks for Senior Solutions Architects (often 150‑200 k base + equity + remote‑work stipend). Prepare a one‑page compensation matrix that ties your proven Snowflake‑specific AI impact (e.g., $X M in client cost reductions) to a justified range, and be ready to discuss equity vesting cadence and remote‑work allowances.