Staff AI Engineer (Global Security)

Job Description

Job Description What is the opportunity? The Staff AI Engineer (Global Security) will be part of Global IT Risk's critical modernization initiative, designing and building advanced AI models that power our IT Risk & Control Assessment platform. Your models will provide data-driven Inherent and Residual Risk Ratings across all RBC applications globally, scaling our assessment capability from today's manual, subset approach to full automation across all domains. These Assessments aim to provide a data-driven Inherent and Residual Risk Rating for every application in RBC across domains of Risk. To conduct a Risk Assessment and their supporting calculations, several key data points are required, including Control Assessments, IT Issues Management findings, Control Environment details and Control Testing results. These data points will drive effectiveness and reliability in the calculation of Residual Risk. The Residual Risk and associated control gaps will then be aggregated to formulate action plans and a risk score for both domain owners and Applications to executives. This solution will scale out our assessment capability across all appcodes globally and domains and will direct leadership decisions on how to reduce the impact and likelihood of regulatory, financial or reputational risks to RBC. What will you do? Design and build AI models that automate control testing, risk assessment, and anomaly detection across IT domains, serving as consultant on the estate of models across the GITR ecosystem Lead model customization and optimization sub-projects/spikes—customizing and optimizing existing and pre-trained models (including LLMs) for RBC's specific risk quantification use cases Drive AI model technical decisions—making informed recommendations on model selection, architecture, and implementation approaches to support the team Act as liaison with model suppliers and stakeholders, evaluating and integrating third-party solutions where appropriate Collaborate closely with the Platform Engineer to ensure models are production-ready and seamlessly integrated into the GITR ecosystem Own model performance monitoring—rigorously evaluating model performance, tuning hyperparameters, validating results, and continuously iterating on improvements based on real-world performance data GITR AI Champion—support the training and adoption of AI into process re-designs, educating teams on model capabilities, limitations, and best practices What do you need to succeed? Must-have Advanced Python programming Expertise in Machine Learning frameworks: Scikit-learn, PyTorch, TensorFlow, Hugging Face Experience with SQL and data manipulation libraries (Pandas, NumPy, Polars Demonstrated experience building and customizing multiple types of AI models: Supervised learning (classification, regression, ensemble methods) Unsupervised learning (clustering, anomaly detection) Natural Language Processing (NLP) and Large Language Models (LLMs) Time-series forecasting and sequenti

Required Skills