Lead Cloud MLOps Engineer, GFT

Job Description

Job Description 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 a highly skilled 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 strong Python and cloud engineering skills with a solid understanding of machine learning model lifecycle management from data preparation to training, validation, registration, and operational inference, can be accountable for the deliverables, and act as the technical lead for a team of engineers. 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 will you do? Design and implement a platform for end-to-end MLOps pipelines to train, test, register, and deploy credit risk machine learning models. 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. Implement CI/CD pipelines using GitHub Actions ensuring consistent and automated deployment processes. Build data preparation and training scripts in Python and PySpark, optimized for performance and scalability on AWS EMR or similar clusters. Manage model artifacts, dependencies, and environments across public cloud and on-premise contexts. Ensure observability and auditability, structured logging, and model performance tracking. Act as the technical lead for a team of engineers What do you need to succeed? Must Have: 5 years of experience in software engineering, data engineering, or MLOps in enterprise-scale or regulated environments. Proven track record of building ML pipelines in production, preferably in financial services or other data-sensitive domains. Practical knowledge of containerization, and infrastructure. Experience collaborating with data scientists and model validators to operationalize, monitor, and maintain models. Understanding of governance and regulatory requirements (e.g., model audit trails, reproducibility). Hands-on expertise with AWS data and ML services e.g.,

Required Skills