RBC Borealis
Finance
LeadMLPlatformEngineer
Neural analysis suggests this role is
optimal for Lead candidates.
“Lead ML Platform Engineer at RBC Borealis. Skills: Machine Learning Platform Engineering, MLOps, DevOps, Cloud Infrastructure, Automation. Designing, building, and optimizing machine learning deployment tools and automation systems that operate the business’s data and ML. Designing and implementing best practices and standards for data and machine learning pipelines across the”
What You'll Achieve.
Bringing ML to enterprise; Revolutionize finance through world-class research, solutions, and a resilient data platform; Seamlessly integrating AI research and data engineering, to solve critical challenges in the financial industry; Building intelligent, and scalable, data-driven solutions that will help communities thrive and drive innovation for our customers across the bank; Make a difference and lasting impact from a local-to-global scale
Industry & Context.
Group Problem Solving
What They're Looking For.
Must Have
5+ years of experience designing and implementing distributed systems and Machine Learning, Working with building and maintaining DevOps pipeline such as Jenkins, GitHub, Previous experience with MLOps orchestration tools such as AirFlow, KubeFlow, Dagster, Flyte, or, In-depth knowledge of various stages of the machine learning application deployment, Experience with building tools and applications to automate various infrastructure and DevOps, Proficiency with programming languages such as Python, Bash, or, Solid understanding of the UNIX operating, Implementing monitoring solutions to identify system bottlenecks and production, Knowledge of professional software engineering best practices for the full software development life cycle, including testing methods, coding standards, code reviews and source control, Hands-on experience building and deploying hybrid environments on-prem and major cloud environments, such as AWS and, Familiarity with machine learning frameworks such as PyTorch, TensorFlow and/or similar
What You'll Do.
and optimizing machine learning deployment tools and automation systems that operate the business’s data and ML
Designing and implementing best practices and standards for data and machine learning pipelines across the
Supporting applications and projects with infrastructure design decision
Building highly scalable
resilient cloud and on-premise systems for hosting machine learning systems using state-of-the-art technologies
How You'll Work.
Team & Collaboration
Collaborating with engineers, and machine learning researchers to automate code analysis, build, integration and deployment of ML; Works collaboratively; Winning together as One RBC
Full Job Description
**_Job Description_** **What 's the opportunity?** We’re looking for an experienced Machine Learning Platform Engineer 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 reinforcement learning, unsupervised learning and computer vision. 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:** * 5+ years of experience designing and implementing distributed systems and Machine Learning systems; * Working with building and maintaining DevOps pipeline such as Jenkins, GitHub actions; * Previous experience with MLOps orchestration tools such as AirFlow, KubeFlow, Dagster, Flyte, or MetaFlow; * In-depth knowledge of various stages of the machine learning application deployment process; * Experience with building tools
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