Senior Solutions Architect – AI/ML, Services Delivery

Snowflake

Senior Solutions Architect developing AI/ML solutions on Snowflake platform for clients. Collaborating with various teams to ensure successful implementation of technology.

Posted 7/2/2026full-timeRemote • North Carolina • 🇺🇸 United StatesSenior💰 $160,000 - $210,000 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformJavaPandasPythonPyTorchScalaScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

Minimum 5 years experience working with customers in a pre-sales or post-sales technical role.
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.

Benefits

Comp & perks

Every Snowflake employee is expected to follow the company’s confidentiality and security standards for handling sensitive data. Snowflake employees must abide by the company’s data security plan as an essential part of their duties.

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SQLPythonAI/MLOpsMachine Learning LifecycleFeature EngineeringModel DeploymentModel ManagementGenerative AIData ScienceCloud Platform Experience

Soft Skills

Presentation SkillsCustomer EngagementCollaborationKnowledge TransferTechnical Guidance