Technical Architect AI/ML
Designs and deploys Snowflake-based AI/ML solutions for customers, including data science and MLOps pipelines, GenAI proofs of concept, and technical enablement. The role requires 10+ years in customer-facing technical work, cloud architecture expertise, and hands-on SQL and programming experience.
About the job
Responsibilities
- Serve as a technical expert on Snowflake for AI/ML workloads.
- Build, deploy, and manage ML pipelines using Snowflake features and ecosystem partner tools.
- Use SQL, Python, and APIs to build hands-on proofs of concept for GenAI and ML workloads.
- Apply best practices and provide knowledge transfer so customers can extend Snowflake capabilities independently.
- Maintain a deep understanding of competitive and complementary AI/ML technologies and vendors.
- Work closely with systems integrator consultants to position and deploy Snowflake in customer environments.
- Guide customers through technical challenges.
- Support Services Delivery team members in developing their expertise.
- Collaborate with Product Management, Engineering, and Marketing to improve Snowflake products and marketing.
- Travel to customer sites approximately 25% of the time.
Requirements
- Minimum 10 years of experience working with customers in a pre-sales or post-sales technical role.
- Experience presenting to technical and executive audiences through whiteboarding, presentations, and demos.
- Thorough understanding of the complete data science lifecycle, including feature engineering, model development, deployment, and management.
- Strong understanding of MLOps technologies and methodologies for deploying and monitoring models.
- Experience with at least one public cloud platform: AWS, Azure, or Google Cloud.
- Experience with at least one data science tool, such as SageMaker, Azure ML, Vertex, Dataiku, DataRobot, H2O, or Jupyter Notebooks.
- Experience with Large Language Models, retrieval, and agentic frameworks.
- Hands-on SQL scripting and experience with Python, R, Java, or Scala.
- Experience with libraries such as Pandas, PyTorch, TensorFlow, or Scikit-learn.
- University degree in computer science, engineering, mathematics, or a related field, or equivalent experience.
Nice to Have
- Experience with Generative AI, LLMs, and vector databases.
- Experience with Databricks or Apache Spark, including PySpark.
- Experience implementing data pipelines with ETL tools.
- Experience working in a data science role.
- Proven success with enterprise software.
- Vertical expertise in financial services, retail, manufacturing, or a similar core industry.
Skills
Snowflake, SQL, Python, R, Java, Scala, AWS, Azure, GCP, MLOps, LLMs, Jupyter Notebooks, pandas, PyTorch, TensorFlow
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