Senior Data/ML Engineer ML Platform

RBC; Toronto; On-site; CAD 80,000 - 100,000; Full time

Job summary

A leading financial services provider in Toronto is looking for a skilled MLOps Engineer. In this role, you will design and build robust machine learning pipelines for model training and inference, supporting both AWS and on-premises setups. You will work closely with data scientists and engineering teams, ensuring sustainability and compliance. Candidates should have experience in ML lifecycle management, CI/CD practices, and strong skills in Python and AWS services. This role promises a vibrant team environment and the opportunity to have a significant impact.

Benefits

Comprehensive Total Rewards Program

Flexible work/life balance options

World-class training program in financial services

Qualifications

• 3+ years of experience in software engineering, data engineering, or MLOps.
• 1+ year experience working with AWS components.
• Experience with containers and infrastructure automation.

Responsibilities

• Design and implement end-to-end reusable MLOps pipelines.
• Build and automate model lifecycle management workflows.
• Develop and integrate a model registry to manage model metadata.

Skills

AWS data and ML services

Python scripting

PySpark

CI/CD practices

Linux systems

Education

Bachelor’s degree in computer science, engineering, data science, or related field

Tools

MLflow

AWS EMR

GitHub Actions

Jenkins

Job description

Overview

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 an 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 Python programming, system design, 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 you will do

• Design and implement end-to-end reusable MLOps pipelines with a team of engineers to train, test, register, and deploy 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, Cloudera Data Platform, or similar.
• Manage model artifacts, dependencies, and environments across AWS and on-premis.
• 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:

• Knowledge of AWS data and ML services e.g., S3, EMR, Lambda, Step Functions, ECS/EKS, SageMaker, CloudWatch, IAM.
• Understanding of model lifecycle management from training and testing to deployment, monitoring, and retraining.
• Experience with 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.
• Knowledge of Python scripting for automation and ML workflow integration.
• Knowledge of PySpark for distributed data processing and model training.

Required Experience

• 3+ years of experience in software engineering, data engineering, or MLOps.
• 1+ year experience working with AWS components
• Experience working with containers and infrastructure automation.
• Experience working with Linux systems, shell scripting, and environment management.

Required Certifications (or equivalent experience)

• AWS Certified Cloud Practitioner - Amazon Web Services
• Bachelor’s degree in computer science, engineering, data science, or related quantitative and technical fields.

Nice to Have

• AWS Certified Machine Learning Engineer Associate, or Certified Solution Architect Associate, or CloudOps/SysOps Engineer Associate
• 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.

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

AWS, SageMaker, PySpark, Python, Spark, Big Data, ML, ML Platforms, Cloud Computing, Data Warehousing, ETL, Data Governance, Linux, Containers

Additional Job Details

Address: RBC WATERPARK PLACE, 88 QUEENS QUAY W:TORONTO

City: Toronto

Country: Canada

Work hours/week: 37.5

Employment Type: Full time

Platform: TECHNOLOGY AND OPERATIONS

Job Type: Regular

Pay Type: Salaried

Posted Date: 2026-02-04

Application Deadline: 2026-02-28

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

Inclusion and Equal Opportunity Employment

At 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.