Solutions Architect – AI/ML

Job Description

About Snowflake:

Snowflake is a rapidly growing company scaling its team to accelerate its growth. They are seeking individuals who share their values, challenge conventional thinking, and drive innovation. Snowflake emphasizes the importance of employee adherence to confidentiality and security standards when handling sensitive data. Data security is a key responsibility for all employees.

Job Description: Solutions Architect – AI/ML

This role is within Snowflake’s Professional Services team, focusing on the creation of new offerings and capabilities for Snowflake’s Data Cloud. The team works directly with customers to expand their use of the Data Cloud, taking data science pipelines from initial concept to deployment and beyond. The position is strategic, advising clients on best practices and implementing data science workloads on Snowflake. This involves designing solutions based on customer needs, coordinating with internal and external teams (including Systems Integrators), and overseeing projects for successful outcomes.

Key Responsibilities:

• Technical Expertise: Serve as a technical expert on all aspects of Snowflake related to AI/ML workloads.
• Best Practices and Advice: Provide customers with best practices and guidance for data science workloads on Snowflake.
• Pipeline Development: Build and deploy ML pipelines using Snowflake features and/or partner tools, based on client requirements.
• Hands-on Development: Work directly with SQL and Python (among other languages) to create Proofs of Concept (POCs) demonstrating implementation techniques and best practices within Snowflake’s data science ecosystem.
• Knowledge Transfer: Ensure proper knowledge transfer to customers, enabling them to independently extend Snowflake’s capabilities.
• Competitive Landscape: Maintain a deep understanding of competitive and complementary AI/ML technologies and vendors, and how Snowflake can be positioned against them.
• Collaboration with Systems Integrators: Work closely with SI consultants to successfully deploy Snowflake in customer environments.
• Technical Problem Solving: Guide clients in resolving technical challenges.
• Team Development: Support other Professional Services team members in developing their expertise.
• Product Improvement: Collaborate with Product Management, Engineering, and Marketing to enhance Snowflake’s products and marketing strategies.

Required Skills & Experience:

• University Degree: Data science, computer science, engineering, mathematics, or a related field (or equivalent experience).
• Experience: Minimum 6 years working with customers in a pre-sales or post-sales technical role.
• Presentation Skills: Excellent presentation skills to both technical and executive audiences (whiteboard sessions and formal presentations).
• Data Science Lifecycle Understanding: Thorough understanding of the entire data science lifecycle (feature engineering, model development, deployment, and management).
• MLOps Expertise: Strong understanding of MLOps, including technologies and methodologies for deploying and monitoring models.
• Cloud Platform Experience: Experience with at least one public cloud platform (AWS, Azure, or GCP).
• Data Science Tool Experience: Experience with at least one data science tool (e.g., AWS Sagemaker, AzureML, Dataiku, Datarobot, H2O, Jupyter Notebooks).
• Scripting Skills: Hands-on scripting experience with SQL and at least one of Python, Java, or Scala.
• Library Experience: Experience with libraries such as Pandas, PyTorch, TensorFlow, SciKit-Learn, or similar.

Bonus Points:

• Experience with Generative AI, LLMs, and Vector Databases.
• Experience with Databricks/Apache Spark.
• ETL pipeline implementation experience.
• Experience in a data science role.
• Successful track record in enterprise software.
• Vertical expertise (e.g., FSI, Retail, Manufacturing).

The job posting also includes salary range and benefits information.