Staff ML Data Engineer Toronto
Summary
Staff-level ML data engineer with RBC Wealth Management's Data & AI team in Toronto: builds feature stores, training datasets, and production data pipelines for AI/ML and BI workloads, including MCP/API integrations and model monitoring. Core stack: Python, SQL, Databricks, Snowflake, Spark, and cloud services.
Build ML-ready infrastructure and scalable pipelines as a Staff ML Data Engineer with RBC Wealth Management in Toronto, supporting AI and analytics.
This Staff-level role bridges machine learning and traditional data engineering across Wealth Management Data & AI. You will prepare feature stores, training datasets, and production data infrastructure while supporting analytics-ready pipelines. Your work contributes to investment capabilities, operational improvements, client experience, and executive decision-making.
Key Responsibilities:
- Architect reliable data foundations for AI/ML and BI workloads
- Build feature stores, training datasets, and ML infrastructure
- Develop pipelines across ingestion, feature engineering, and model serving
- Implement MCP and API integrations with production systems
- Monitor pipelines and models for reliability and compliance
Requirements:
- Bachelor’s degree in a related technical field
- Five-plus years in ML data engineering or related roles
- Strong Python, SQL, MCP, API, and pipeline experience
- Experience with Databricks, Snowflake, Spark, and cloud services
- Knowledge of feature stores, RAG, vector databases, and LLM serving
Apply staff-level data engineering expertise across Python, SQL, ML infrastructure, and modern cloud platforms at RBC Wealth Management.