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-premise 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 through training, validation, registration, and operational inference.

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 end-to-end MLOps pipelines to train, test, register, and deploy credit risk machine learning models.
• Build and automate model lifecycle management workflows including versioning, promotion, approval, and deprecation.
• 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, AWS Step Functions, stonebranch, or Prefect.
• Implement CI/CD pipelines using GitHub Actions, Jenkins, or AWS CodePipeline, 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 AWS and on-premise contexts.
• Ensure strong observability and auditability through structured logging, metrics, and model performance tracking.
• Collaborate with DevOps and data engineering teams to ensure secure integration, data governance, and production readiness.

What do you need to succeed?

Must Have:

• Hands-on expertise with AWS data and ML services e.g., S3, EMR, Lambda, Step Functions, ECS/EKS, SageMaker, CloudWatch, IAM.
• Experience building and maintaining model registries, versioning systems, and artifact repositories (e.g., MLflow, SageMaker, DVC).
• 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.
• Familiarity with hybrid deployment environments (AWS and on-prem) and related networking/security considerations.
• Proficiency in Python for production-quality scripting, automation, and ML workflow integration.
• Strong experience with PySpark for distributed data processing and model training.

Required Experience

• 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.
• Experience managing model artifacts and metadata for auditability and compliance.
• Practical knowledge of containerization (Docker) and infrastructure automation (Terraform, CloudFormation).
• Strong background in Linux-based systems, shell scripting, and environment management.
• Experience collaborating with data scientists and model validators to operationalize, monitor, and maintain models.
• Understanding of data governance and regulatory requirements (e.g., model audit trails, reproducibility).

Required Certifications (or equivalent experience)

• AWS Certified Solutions Architect Associate or higher. (must have)
• AWS Certified Machine Learning Engineer Associate or AWS Certified Machine Learning Specialty (strongly preferred)
• AWS Certified DevOps Engineer Professional (preferred)
• Bachelor’s or master’s degree in computer science, Engineering, Data Science, or related quantitative and technical field.
• AWS CloudOps/SysOps Engineer Associate (nice to have)
• Databricks Certified Data Engineer Associate/Professional or equivalent PySpark certification. (nice to have)
• Python PCAP or Terraform Associate Certificate. (nice to have)

Nice to Have:

• Experience implementing model monitoring and drift detection.
• Familiarity with distributed training and parallel compute 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.
• Familiarity with secure data exchange patterns between cloud and on-prem environments.
• Exposure to observability stacks (ELK, Prometheus, Grafana, CloudWatch)

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

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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:

2025-07-16

Application Deadline:

2025-11-30

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