Role : Snowflake ML Architect (Cortex)
Location : Atlanta Ga / Boston Ma
Position Type : Full-Time
Experience : 12–15+ years overall; 4–5+ years in Data / ML Architecture
About the Role
We're seeking an experienced Snowflake ML Architect with hands-on expertise in Snowflake Cortex, Snowpark ML, and Generative AI integration.
This role is ideal for someone who combines deep data architecture expertise with machine learning and GenAI solutioning experience — capable of designing, implementing, and scaling AI / ML workloads natively within the Snowflake platform.
You will define end-to-end architectures for data pipelines, model development, and deployment while leveraging Cortex and Snowpark to operationalize AI inside Snowflake.
Key Responsibilities
- Architect and implement end-to-end ML and GenAI solutions within Snowflake using Cortex and Snowpark ML.
- Design and manage data pipelines, feature engineering, model training, and inference workflows.
- Integrate LLMs and vector search for Retrieval Augmented Generation (RAG) and semantic search use cases.
- Collaborate with data engineering and data science teams to standardize ML development and deployment practices.
- Build and optimize data models, UDFs, and stored procedures in Python and Snowflake SQL.
- Leverage Streamlit and Snowflake Native Apps for building ML-driven dashboards or user interfaces.
- Ensure scalability, performance, and governance of ML workloads in Snowflake.
- Stay up to date with new Cortex and Snowpark ML capabilities and recommend best practices for adoption.
- Mentor developers and guide teams in data-to-AI pipeline architecture and MLOps implementation.
Required Skills & Qualifications
Strong hands-on experience with Snowflake, including performance tuning, data modeling, and security best practices.Expertise in Snowflake Cortex, Snowpark ML, Streamlit, and Python-based UDFs.Solid programming skills in Python and experience with ML frameworks (scikit-learn, PyTorch, TensorFlow, etc.).Practical exposure to LLMs, GenAI, and RAG-based architectures.Experience integrating AI / ML models into production using Snowflake's native capabilities.Strong understanding of cloud data ecosystems — AWS, Azure, or GCP.Familiarity with MLOps, CI / CD, containerization (Docker / Kubernetes), and orchestration tools (Airflow, dbt, etc.).Excellent communication, documentation, and leadership skills.Preferred Qualifications
Experience with data migration or modernization to Snowflake.Snowflake Advanced Architect / SnowPro Core certification.Understanding of data governance, security, and cost optimization in cloud environments.Prior exposure to AI use cases like chatbot design, summarization, or recommendation