Machine Learning Platform Engineer II

Key Skills

Apache Airflow, AWS, Azure, C++, Continuous Integration & Continuous Delivery - CI/CD, Java Programming, Kubeflow, Machine learning techniques, OLAP, Python Programming, SQL

Job Description

We’re looking for an experienced Machine Learning Platform Engineer II who will bring focus and subject-matter expertise around designing and implementing machine learning infrastructure and automation tools (MLOps and DevOps). This is a unique opportunity to grow in the world of machine learning infrastructure and work with a team of passionate individuals committed to the mission of bringing ML to enterprise.

At RBC Borealis, you’ll be joining a team that works directly with leading researchers in machine learning, has access to rich and massive datasets, and offers the computational resources to support ongoing development in areas such as Generative AI, unsupervised learning and reinforcement learning. You can find out more about our research areas at rbcborealis.com.

Your responsibilities include:

• Designing, building, and optimizing machine learning deployment tools and automation systems that operate the business’s data and ML applications.
• Designing and implementing best practices and standards for data and machine learning pipelines across the organization.
• Collaborating with engineers, and machine learning researchers to automate code analysis, build, integration and deployment of ML applications.
• Supporting applications and projects with infrastructure design decision, and monitoring solution.
• Building highly scalable, resilient cloud and on-premise systems for hosting machine learning systems using state-of-the-art technologies.

You're our ideal candidate if you have:

• A Bachelor’s degree in a related discipline such as Computer Science or Software Engineering.
• Proficiency in a professional programming language such as Python, Java or C++.
• Strong knowledge of tools and practices relating to the full ML model development life cycle, including writing modular and scalable code, experimentation, testing, CI/CD, and model monitoring, hosting and inference.
• Hands-on experience with transforming large-scale data sets using SQL and tools like OLAP databases and/or Spark.
• Previous experience with MLOps orchestration tools such as AirFlow, KubeFlow, Dagster, Flyte, or MetaFlow.
• Experience implementing monitoring solutions to identify system bottlenecks and production issues.

Nice to Have:

• Hands-on experience building and deploying platforms for ML users, whether on-premise or major cloud environments, such as AWS and Azure.
• Familiarity with ML frameworks such as PyTorch, TensorFlow and/or similar.
• Strong fundamentals in one or more of Networking, Storage and Distributed Systems.

What's in it for you?

Become part of a team that thinks progressively and works collaboratively. We care about seeing each other reach full potential.

A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and stock options where applicable.

Leaders who support your development through coaching and managing opportunities.

Ability to make a difference and lasting impact from a local-to-global scale.

About RBC Borealis

RBC Borealis is the driving force behind Royal Bank of Canada’s AI and data innovation. As engineers, scientists, and product experts on a mission to revolutionize finance through world-class research, solutions, and a resilient data platform. With locations across Toronto, Waterloo, Montreal, Calgary, and Vancouver, we’re at the forefront of AI research and platform development. With a focus on cutting-edge research in areas like time series forecasting, causal machine learning, and responsible AI, we are seamlessly integrating AI research and data engineering, to solve critical challenges in the financial industry. We are building intelligent, and scalable, data-driven solutions that will help communities thrive and drive innovation for our customers across the bank.