Specialist - Architecture

1 Feature Engineering Pipeline Management Design and scale feature pipelines using Snowpark PythonSQL to transform raw datainto production ready ML features

Implement and manage the Snowflake Feature Store as a centralized governed repository for batch training and lowlatency online inference

Optimize data ingestion and processing costs using Snowflakes elastic compute multicluster warehouses and search optimization services2 Model Training Orchestration Establish scalable ML training infrastructure using Snowflake Notebooks and Container Runtimes CPUGPU instances without data egress

Orchestrate endtoend ML workflows and retraining schedules using Snowflake Tasksand Streams Integrate opensource frameworks Scikit Learn PyTorch XGBoost into Snowflake ecosystem via Snowpark ML3 Model Deployment Serving Manage the Snowflake Model Registry for cataloging versioning metadata logging andlifecycle governance Development Staging Production Deploy models for batch and realtime inference using User Defined Functions UDFsor containerized services

Integrate LLMs and GenAI applications using Snowflake Cortex AI functions4 MLOps Platform Setup Operations Design and build endtoend MLOps platform including CICD pipelines model registry experiment tracking and feature store

Implement automated model training validation deployment and monitoring workflows

Establish reusable ML pipeline templates and accelerators for development teams5 CICD Automation Infrastructure Build automated CICD pipelines using Terraform for model testing validation and promotion

Implement Infrastructure as Code IaC using Terraform to provision Snowflake resources securely and repeatably

Enforce data and model governance through Snowflakes native securityrowlevel security data masking RBAC6 Monitoring Observability Deploy ML monitoring frameworks to track model performance data driftand prediction latency

Design automated retraining loops triggered by accuracy drops or data distribution shifts

Build operational dashboards using Streamlit in Snowflake for realtime model health visibility7 Snowflake ML Standards Development Define and enforce ML engineering standards patterns and best practices within Snowflake Develop Snowflake native ML workflows for feature engineering training and inference

Manage Snowflake compute warehouse configurations optimized for ML workloads8 ML Development Assistance Collaborate with data scientists to product ionize models from prototype toproductiongrade code Build shared libraries utilities and SDKs to accelerate model development

Conduct code reviews and enforce coding testing standards for ML codebases9 Governance Documentation Establish model versioning lineage tracking and reproducibility standards

Document platform architecture runbooks and onboarding guides

Ensure compliance with RBCs data governance and security policies