Lead Cloud MLOps Engineer, GFT

RBC • Vancouver, BC

1d • Onsite

About The Position

The opportunity is with Global Functions Technology (GFT), part of RBC’s Technology and Operations division. GFT collaborates with partners across the company to deliver innovative and transformative IT solutions for clients in Risk, Finance, HR, CAO, Audit, Legal, Compliance, Financial Crime, Capital Markets, Personal and Commercial Banking, and Wealth Management. They also lead the development of digital tools and platforms to enhance collaboration. The role is for a highly skilled MLOps Engineer to help design and build a production-grade machine learning pipeline for financial risk model training and inference. This pipeline will support model training/testing/inference using Python and PySpark on public cloud (AWS) and on-premises infrastructure. The ideal candidate combines strong Python and cloud engineering skills with a solid understanding of machine learning model lifecycle management, can be accountable for deliverables, and act as the technical lead for a team of engineers. The role involves collaborating closely with data scientists, DevOps, and risk IT teams to build a reliable, automated, and auditable MLOps platform that meets enterprise standards for security, governance, and scalability.

Requirements

• 5+ years of experience in software engineering, data engineering, or MLOps in enterprise-scale or regulated environments.
• Proven track record of building ML pipelines in production, preferably in financial services or other data-sensitive domains.
• Practical knowledge of containerization, and infrastructure.
• Experience collaborating with data scientists and model validators to operationalize, monitor, and maintain models.
• Understanding of governance and regulatory requirements (e.g., model audit trails, reproducibility).
• Hands-on expertise with AWS data and ML services e.g., S3, Lambda, ECS/EKS, SageMaker, CloudWatch, IAM.
• Solid understanding of model lifecycle management from training and testing to deployment, monitoring, and retraining.
• Strong grasp of CI/CD practices, using tools like GitHub Actions, Jenkins, or CodePipeline.
• Proficiency in Python for production-quality scripting, automation, and ML workflow integration.
• Familiarity with hybrid deployment environments (public cloud and on-prem) and related networking/security considerations.

Nice To Haves

• Proficiency in PySpark for distributed data processing.
• Experience implementing model monitoring and drift detection.
• Familiarity with distributed training and parallel computation frameworks (Ray, Spark, Dask).
• Experience with feature stores, data lineage, or metadata tracking systems.
• Exposure to financial risk modeling workflows.
• Working knowledge of container orchestration (Kubernetes, OpenShift) and hybrid deployments.
• Exposure to observability stacks (ELK, Prometheus, Grafana, CloudWatch).
• Experience managing model artifacts and metadata for auditability and compliance, MLflow or KServer.
• Bachelor’s or master’s degree in computer science, Engineering, Data Science, or related quantitative and technical field.
• AWS Certified Solutions Architect Associate
• AWS Certified Machine Learning Engineer Associate

Responsibilities

• Design and implement a platform for end-to-end MLOps pipelines to train, test, register, and deploy credit risk machine learning models.
• Develop and integrate a model registry (e.g., MLflow, SageMaker Model Registry, or custom solution) to manage model metadata, lineage, and reproducibility.
• Orchestrate data and training workflows using tools such as Airflow.
• Implement CI/CD pipelines using GitHub Actions ensuring consistent and automated deployment processes.
• Build data preparation and training scripts in Python and PySpark, optimized for performance and scalability on AWS EMR or similar clusters.
• Manage model artifacts, dependencies, and environments across public cloud and on-premise contexts.
• Ensure observability and auditability, structured logging, and model performance tracking.
• Act as the technical lead for a team of engineers.

Benefits

• bonuses
• flexible benefits
• competitive compensation
• commissions
• stock where applicable
• Leaders who support your development through coaching and managing opportunities
• Ability to make a difference and lasting impact
• Work in a dynamic, collaborative, progressive, and high-performing team
• A world-class training program in financial services
• Flexible work/life balance options
• Opportunities to do challenging work

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Number of Employees: 5,001-10,000 employees