Snowflake ML Architect with Cortex in Atlanta

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Job Description

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 (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 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 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.

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

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