Senior MLOps Consultant

Position Details:

Title: Senior MLOps Consultant

Location: Toronto, ON (Remote Role)

Type: Full-Time

Duration: 12+ Months with possible extension

Start Date: ASAP

Job Description:

Feature Engineering Pipeline Management

• Design and scale feature pipelines using Snowpark PythonSQL to transform raw data into productionready 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 services

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 Tasks and Streams
• Integrate opensource frameworks (ScikitLearn, PyTorch, XGBoost) into the Snowflake ecosystem via Snowpark ML

Model Deployment Serving

• Manage the Snowflake Model Registry for cataloging versioning metadata logging and lifecycle governance Development Staging Production
• Deploy models for batch and realtime inference using UserDefined Functions UDFs or containerized services
• Integrate LLMs and GenAI applications using Snowflake Cortex AI functions

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 teams

CICD Automation Infrastructure

• Build automated CICD pipelines using Terraform for model testing validation and promotion
• Implement InfrastructureasCode IaC using Terraform to provision Snowflake resources securely and repeatably
• Enforce data and model governance through Snowflake's native security rowlevel security data masking RBAC

Monitoring Observability

• Deploy ML monitoring frameworks to track model performance data drift and 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 visibility

Snowflake ML Standards Development

• Define and enforce ML engineering standards patterns and best practices within Snowflake
• Develop Snowflakenative ML workflows for feature engineering training and inference
• Manage Snowflake computewarehouse configurations optimized for ML workloads

ML Development Assistance

• Collaborate with data scientists to productionize models from prototype to productiongrade code
• Build shared libraries utilities and SDKs to accelerate model development
• Conduct code reviews and enforce coding and testing standards for ML codebases

Governance Documentation

• Establish model versioning lineage tracking and reproducibility standards
• Document platform architecture runbooks and onboarding guides
• Ensure compliance with RBC's data governance and security policies

Thanks & Regards

Cloudious LLC