What is the opportunity? Join CMTC at an inflection point where commercial banking meets the frontier of artificial intelligence. As a Machine Learning Engineer on our team, you will be at the center of RBC's strategic push to embed AI and Data into core commercial banking workflows, building models and platforms that directly shape how commercial clients experience banking. This is not a research role, so you will own end‑to‑end delivery of production ML systems that drive real business outcomes across risk, client intelligence, and automation. You will collaborate with data scientists, engineers, and business leaders across the organization, with access to rich, complex datasets and meaningful problems at scale. If you want to build AI that matters and move from prototypes to deployed, impactful solutions, this is the role for you. What will you do? Design, build, and deploy production‑grade machine learning models that power AI use cases across commercial banking (transaction intelligence, client intelligence, automation, anomaly detection) Collaborate with data scientists and data engineers to translate experimental models into scalable, maintainable ML pipelines and services Partner with business stakeholders to understand commercial banking problems and frame them as solvable ML problems Develop and maintain MLOps infrastructure on‑prem, including model training, versioning, monitoring, and retraining pipelines Ensure model reliability, fairness, and compliance with RBC's risk and governance standards Work cross‑functionally with engineering, product, and compliance teams to integrate ML solutions into existing banking platforms Continuously evaluate emerging AI/ML tools and frameworks to improve team velocity and solution quality Be a power user of latest LLM models for daily workload and contribute to CMTC's AI roadmap by identifying new opportunities where ML can create measurable business value What do you need to succeed? Must have Hands‑on experience building and deploying ML models in a production environment Proficiency in Python and core ML libraries (scikit‑learn, PyTorch, TensorFlow, or equivalent) Experience with MLOps practices — feature stores, model registries, CI/CD for ML, cloud deployments, monitoring and drift detection Strong understanding of supervised/unsupervised learning, model evaluation, and statistical fundamentals Ability to communicate complex model outputs clearly to non‑technical business stakeholders Experience working with large, structured datasets and cloud platforms (OCP, AWS, etc.) Nice‑to‑have Experience in financial services, banking, or regulated industries Familiarity with NLP, LLMs, or generative AI applications in an enterprise context including active production deployment Knowledge of responsible AI principles; explainability, bias detection, model governance Experience in a large, matrixed organization with multiple competing priorities What’s in it for you? 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 management 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 Opportunities to do challenging work At RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and win together as One RBC. We strive to deliver a workplace based on respect, belonging and opportunity for all. #J-18808-Ljbffr