Model Risk AI Governance Engineer

AI Governance & Lifecycle Oversight

• Support the implementation and continuous improvement of AI governance frameworks and control processes.
• Facilitate governance reviews, approval workflows, and control checkpoints throughout the AI/ML lifecycle.
• Assist in conducting risk assessments for AI and machine learning use cases, ensuring risks are identified, documented, and appropriately mitigated.
• Promote adherence to internal governance standards, policies, and regulatory requirements.

AI Inventory & Model Management

• Maintain and enhance centralized repositories used to track AI models and AI-enabled solutions.
• Ensure AI assets are accurately documented, classified, and managed throughout their lifecycle.
• Support the onboarding of new AI initiatives using established materiality, criticality, and risk-based assessment frameworks.
• Track model and AI solution status across development, validation, deployment, monitoring, and retirement stages.

Technical Controls & Engineering Support

• Collaborate with AI/ML engineering teams to ensure governance and risk requirements are incorporated into development processes.
• Support implementation of monitoring, logging, explainability, and model performance controls.
• Develop and enhance automation solutions for governance workflows, inventory management, and reporting activities using Python or similar technologies.
• Assist in establishing data lineage, traceability, and auditability across AI systems and supporting infrastructure.

Monitoring, Reporting & Risk Analytics

• Create and maintain dashboards and reporting solutions that provide visibility into AI inventory, governance activities, and risk metrics.
• Monitor key indicators such as model performance, drift, fairness, explainability, and operational risks.
• Support preparation of management and governance committee reports.
• Identify control gaps, track remediation activities, and contribute to ongoing risk management initiatives.

Stakeholder Collaboration

• Partner with business, technology, data science, compliance, validation, and audit teams to support effective AI governance practices.
• Assist in awareness and training initiatives related to responsible AI, governance standards, and lifecycle management requirements.
• Support internal reviews and regulatory engagements relating to AI risk and governance processes.

Key Requirements

Education

• Bachelor’s or master’s degree in computer science, Data Science, Artificial Intelligence, Engineering, Mathematics, Statistics, or a related quantitative discipline.

Experience

• 5+ years of experience in AI engineering, machine learning, data science, model governance, model risk management, or related disciplines.
• Experience supporting AI/ML lifecycle management, governance frameworks, or risk oversight activities.
• Exposure to model inventories, metadata management, model documentation, or governance processes is highly desirable.
• Experience working within regulated industries such as banking, financial services, insurance, or healthcare is advantageous.

Technical Skills

• Strong programming skills in Python and experience developing automation solutions.
• Solid understanding of machine learning concepts, model development processes, evaluation techniques, and monitoring practices.
• Familiarity with model governance, AI risk management, or responsible AI frameworks.
• Understanding of data governance principles, including lineage, traceability, and documentation.
• Experience with reporting and visualization tools such as Power BI or Tableau.
• Knowledge of model monitoring, explainability, fairness, and model risk concepts is preferred.