MLOps Engineer – Snowflake

Location: Remote – Canada

Employment Type: Full time

Salary: Up to CAD $150,000 per year

Job Summary

We are seeking an experienced MLOps Engineer to design, build, and operate scalable machine learning infrastructure using Snowflake and Snowflake ML. The ideal candidate will have strong experience in ML pipeline engineering, model deployment, CI/CD automation, infrastructure as code, monitoring, and productionizing machine learning workloads.

You will work closely with Data Scientists, ML Engineers, Data Engineers, and Platform teams to develop reliable, governed, and reusable MLOps capabilities from experimentation through production deployment.

Key Responsibilities

- Feature Engineering & Pipeline Management

- Design and scale feature engineering pipelines using Snowpark, Python, and SQL to transform raw data into production-ready ML features.
- Implement and manage the Snowflake Feature Store as a centralized, governed repository for batch training and low-latency online inference.
- Optimize data ingestion and processing costs using Snowflake elastic compute, multi-cluster warehouses, and search optimization services.
- Develop reusable feature engineering frameworks and pipeline components.

2. Model Training & Orchestration
- Establish scalable ML training infrastructure using Snowflake Notebooks and Container Runtime, including CPU/GPU-based workloads.
- Orchestrate end-to-end ML workflows and automated retraining schedules using Snowflake Tasks and Streams.
- Integrate open-source ML frameworks including Scikit-learn, PyTorch, and XGBoost with the Snowflake ecosystem through Snowpark ML.
- Build repeatable training pipelines with automated validation and experiment tracking.

3. Model Deployment & Serving
- Manage the Snowflake Model Registry for model cataloging, versioning, metadata management, and lifecycle governance across Development, Staging, and Production environments.
- Deploy models for batch and real-time inference using UDFs and containerized services.
- Implement secure and scalable model-serving patterns.




- Integrate LLM and Generative AI applications using Snowflake Cortex AI capabilities.

4. MLOps Platform Setup & Operations
- Design and build an end-to-end MLOps platform, including CI/CD pipelines, model registry, experiment tracking, feature store, and monitoring.
- Implement automated workflows for model training, validation, deployment, monitoring, and retraining.
- Develop reusable ML pipeline templates, frameworks, and accelerators for development teams.
- Establish standardized development-to-production workflows for machine learning applications.

5. CI/CD Automation & Infrastructure
- Build automated CI/CD pipelines for ML model testing, validation, packaging, and promotion.
- Implement Infrastructure as Code (IaC) using Terraform to provision and manage Snowflake resources securely and consistently.
- Automate environment configuration and deployment processes.
- Enforce data and model governance using Snowflake security capabilities including RBAC, row-level security, and data masking.

6. Monitoring & Observability
- Implement ML monitoring frameworks to track model performance, data drift, model drift, and prediction latency.
- Design automated retraining workflows triggered by performance degradation or significant changes in data distributions.
- Develop operational dashboards using Streamlit in Snowflake for real-time visibility into model and pipeline health.
- Establish alerting and incident-response processes for production ML workloads.

7. Snowflake ML Standards & Development
- Define and enforce ML engineering standards, design patterns, and best practices within the Snowflake ecosystem.
- Develop Snowflake-native workflows for feature engineering, model training, deployment,



and inference.
- Configure and optimize Snowflake compute warehouses for ML workloads.
- Establish reusable architecture patterns and engineering guidelines for MLOps development.

8. ML Development Support
- Collaborate with Data Scientists and ML Engineers to productionize models from prototypes into production-grade solutions.
- Build shared libraries, utilities, SDKs, and reusable components to accelerate ML development.
- Conduct code reviews and enforce coding, testing, security, and quality standards.
- Provide technical guidance on production ML architecture and operational best practices.

9. Governance & Documentation
- Establish standards for model versioning, lineage tracking, reproducibility, and lifecycle management.
- Maintain documentation for MLOps platform architecture, workflows, runbooks, and onboarding procedures.
- Ensure ML platforms and workflows comply with organizational data governance, security, and regulatory requirements.
- Support auditability and traceability across the ML model lifecycle.

Required Qualifications
- 5+ years of experience in MLOps, ML Engineering, Data Engineering, or a related field.
- Strong hands-on experience with Snowflake and Snowpark.
- Experience building and managing production-grade machine learning pipelines.
- Strong Python and SQL programming skills.
- Experience with Snowflake ML, Feature Store, Model Registry, Tasks, Streams, Notebooks, and Container Runtime.
- Experience with ML frameworks such as Scikit-learn, XGBoost, and/or PyTorch.
- Strong experience with CI/CD and DevOps practices.
- Hands-on experience with Terraform and Infrastructure as Code.
- Experience with ML model deployment, monitoring, drift detection, and retraining.
- Understanding of cloud-based ML infrastructure and distributed data processing.
- Experience implementing security and governance controls such as RBAC, data masking, and row-level security.
- Strong software engineering, testing, troubleshooting, and code-review practices

📌 MLOps Engineer – Snowflake (Canada)
🏢 Enterprise Solutions
📍 Canada