Rbc
Financial Services
LeadCloudMLOpsEngineer,GFT
Neural analysis suggests this role is
optimal for Lead candidates.
“Lead Cloud MLOps Engineer, GFT at Rbc. Skills: Cloud MLOps, Machine Learning Pipelines, AWS, Python, MLOps Platform Development, Model Lifecycle Management, CI/CD. 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”
What You'll Achieve.
Deliver high-performing applications; Build a reliable, automated, and auditable MLOps platform that meets enterprise standards for security, governance, and scalability
Industry & Context.
What They're Looking For.
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., S3, Lambda, ECS/EKS, SageMaker, CloudWatch, IAM, Solid understanding of model lifecycle management from training and testing to deployment, monitoring, and retraining, grasp of CI/CD practices, using tools like GitHub Actions, Jenkins, or CodePipeline, Proficiency in Python for production-quality scripting, automation, and ML workflow integration, Familiarity with hybrid deployment environments (public cloud and on-prem) and related networking/security considerations
Nice to Have
Proficiency in PySpark for distributed data processing, Experience implementing model monitoring and drift detection, Familiarity with distributed training and parallel computation frameworks (Ray, Spark, Dask), Experience with feature stores, data lineage, or metadata tracking systems, Exposure to financial risk modeling workflows, Working knowledge of container orchestration (Kubernetes, OpenShift) and hybrid deployments, Exposure to observability stacks (ELK, Prometheus, Grafana, CloudWatch), Experience managing model artifacts and metadata for auditability and compliance, MLflow or KServer, Bachelor’s or master’s degree in computer science, Engineering, Data Science, or related quantitative and technical field, AWS Certified Solutions Architect Associate, AWS Certified Machine Learning Engineer Associate
What You'll Do.
Design and implement a platform for end-to-end MLOps pipelines to train
and deploy credit risk machine learning models
Develop and integrate a model registry (e. g.
SageMaker Model Registry
or custom solution) to manage model metadata
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
and environments across public cloud and on-premise contexts
Ensure observability and auditability
and model performance tracking
Act as the technical lead for a team of engineers
How You'll Work.
Team & Collaboration
Collaborate with partners from across the company; Collaborate closely with data scientists, DevOps, and risk IT teams; Collaborate with data scientists and model validators
Full 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 d
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