Lead Cloud MLOps Engineer, GFT

Job Description

What is the opportunity?

Are you a talented, creative, and results-driven professional who thrives on delivering high-performing applications? Come join us!

Global Functions Technology (GFT) is part of RBC’s Technology and Operations division. GFT’s impact is far-reaching as we collaborate with partners from across the company to deliver innovative and transformative IT solutions. Our clients represent Risk, Finance, HR, CAO, Audit, Legal, Compliance, Financial Crime, Capital Markets, Personal and Commercial Banking and Wealth Management. We also lead the development of digital tools and platforms to enhance collaboration.

We are looking for a highly skilled MLOps Engineer to help design and build a production-grade machine learning pipeline for financial risk model training and inference.

The pipeline will support model training/testing/inference using Python and PySpark, on public cloud (AWS) and on-premises infrastructure.

This role is ideal for an engineer who combines strong Python and cloud engineering skills with a solid understanding of machine learning model lifecycle management from data preparation to training, validation, registration, and operational inference, can be accountable for the deliverables, and act as the technical lead for a team of engineers.

You’ll collaborate 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.

What will you do?

• 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

What do you need to succeed?

Must Have:

• 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 have:

• 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

What’s in it for you?

We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.

• A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and 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

#LI-Post

#TechPJ

Job Skills

Amazon SageMaker, Amazon SageMaker, Apache Airflow, Apache Spark, AWS Architecture, Big Data Management, Big Data Platforms, Big Data Solutions, Big Data Tools, Cloud Computing, Credit Risk Management, Database Development, Data Mining, Data Warehousing (DW), Distributed Computing, ETL Development, Generative AI, Kubernetes, Liquidity Risk, Machine Learning Model Management, Machine Learning Operations, Market Risk Management, MLflow, Pandas Python Library, PySpark {+ 6 more}

Additional Job Details

Address:

410 GEORGIA ST W, FLOOR 3:VANCOUVER

City:

Vancouver

Country:

Canada

Work hours/week:

37.5

Employment Type:

Full time

Platform:

TECHNOLOGY AND OPERATIONS

Job Type:

Regular

Pay Type:

Salaried

Posted Date:

2026-04-30

Application Deadline:

2026-05-31

Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above

Our Employment Opportunities

At RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and winning together as One RBC. We believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.

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RBC is presently inviting candidates to apply for this existing vacancy. Applying to this posting allows you to express your interest in this current career opportunity at RBC. Qualified applicants may be contacted to review their resume in more detail.