AI/ML Engineer, Platform

About The Company
RBC Borealis is a pioneering division within the Royal Bank of Canada dedicated to advancing artificial intelligence and data innovation. As part of Canada's largest financial institution, RBC Borealis brings together a team of talented architects, engineers, scientists, and product specialists committed to transforming the financial industry through cutting-edge research, innovative solutions, and a resilient data platform. With multiple locations across Toronto, Waterloo, Montreal, Calgary, and Vancouver, the organization is at the forefront of AI research and platform development. The team focuses on areas such as time-series forecasting, causal machine learning, and responsible AI, seamlessly integrating AI research with data engineering to address critical challenges in finance. The goal is to build intelligent, scalable, data-driven solutions that foster community growth and drive innovation for clients across the bank.
About The Role
We are seeking an experienced AI/ML Platform Engineer to join our dynamic team. This role is pivotal in driving the reuse and delivery of enterprise Data, MLOps/AIOps, and DevOps platform products and services. The successful candidate will be responsible for designing, building, and scaling infrastructure and automation tools that support our line-of-business data and AI/ML applications. Working collaboratively with leading researchers and engineers, you will leverage state-of-the-art technologies to develop scalable and resilient solutions across both cloud and on-premises environments. This position offers an exciting opportunity to contribute to innovative projects involving reinforcement learning, computer vision, and unsupervised learning, utilizing rich datasets and significant computational resources. Your work will directly impact how the organization accelerates solution delivery, ensures best practices, and maintains robust, scalable, and efficient AI/ML pipelines.
Qualifications

• Strong experience designing and implementing distributed systems and machine learning solutions
• Experience building and maintaining DevOps pipelines such as Helios 2.0 and GitHub Actions
• Previous experience with MLOps orchestration tools such as Airflow, Kubeflow, or Dagster
• In-depth knowledge of all stages of the machine learning application deployment process
• Experience developing tools and applications to automate infrastructure and DevOps tasks
• Proficiency in programming languages such as Python, Bash, or JavaScript
• Experience implementing monitoring solutions to identify system bottlenecks and production issues
• Knowledge of software engineering best practices, including testing, coding standards, code reviews, and source control management
• Hands-on experience deploying hybrid environments on-premises and cloud platforms like AWS and Azure
• Familiarity with machine learning frameworks such as PyTorch and TensorFlow
• Strong systems thinking and problem-solving skills, with a holistic approach to solution design

Responsibilities

• Champion the reuse of Data, MLOps/AIOps, and DevOps platform products, services, and patterns to accelerate solution delivery for business partners and domain teams
• Design, build, and optimize deployment tools and automation systems for AI/ML applications and enterprise data pipelines
• Establish and enforce best practices and standards for data and machine learning pipelines organization-wide
• Collaborate with engineers and AI/ML researchers to automate code analysis, build, integration, and deployment processes
• Support infrastructure design decisions and implement monitoring solutions to ensure system health and performance
• Deliver reusable platform products that facilitate rapid onboarding and consistent delivery for business teams
• Apply adaptive productization processes to ensure solutions are scalable, robust, and aligned with evolving business needs
• Practice “fail fast” principles to identify issues quickly, enabling rapid iteration and continuous improvement
• Utilize systems thinking to approach complex problems holistically, considering organizational and technical contexts
• Break down complex requirements into manageable domains, driving solutions from a top-down perspective

Benefits

• Opportunity to work within a progressive and collaborative team environment
• Comprehensive Total Rewards Program including bonuses, flexible benefits, and competitive compensation packages
• Equity options such as stock options where applicable
• Leadership support for professional development through coaching and growth opportunities
• Ability to make a meaningful impact on a global scale within a leading financial institution
• Access to cutting-edge AI research, datasets, and computational resources
• Work in a diverse and inclusive environment that values innovation and collaboration

Equal Opportunity
RBC is an equal opportunity employer committed to fostering an inclusive workplace. We welcome applications from all qualified candidates regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, Indigenous status, or any other protected characteristic. We are dedicated to providing accommodations during the application process upon request, ensuring equal access and opportunity for all applicants.