AI Technology Risk Manager

Our vision for the future is based on the idea that transforming financial lives starts by giving our people the freedom to transform their own. We have a flexible work environment, and fluid career paths. We not only encourage but celebrate internal mobility. We also recognize the importance of purpose, well-being, and work-life balance. Within Empower and our communities, we work hard to create a welcoming and inclusive environment, and our associates dedicate thousands of hours to volunteering for causes that matter most to them.

Chart your own path and grow your career while helping more customers achieve financial freedom. Empower Yourself.

***Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time, including CPT/OPT.***

The AI Model Risk Manager will identify, assess, validate, and monitor model risks associated with machine learning, generative AI, agentic AI, and other advanced analytical models. This individual contributor will execute model risk assessments and validation activities, perform independent review and challenge, and support the governance of AI and machine learning models throughout their lifecycle.

The role will partner closely with data science, engineering, product, business, technology risk, and second-line risk teams to help ensure models are appropriately documented, validated, inventoried, monitored, and governed in accordance with enterprise model risk management standards and risk appetite. The AI Model Risk Manager will translate complex model methodologies, limitations, assumptions, and performance considerations into clear risk conclusions and actionable recommendations for model owners, leadership, and risk governance forums.
What you will do:

- Execute model risk assessments and validation activities for machine learning, generative AI, large language models, agentic AI, and other advanced analytical models across the model lifecycle, including development, implementation, use, monitoring, change management, and retirement.

- Perform independent review and challenge of AI and machine learning models, including model design and methodology, intended use, data suitability, training and testing approaches, performance metrics, validation methods, limitations, assumptions, controls, and ongoing monitoring.

- Assess model conceptual soundness and evaluate whether model development, testing, validation, and monitoring activities are appropriate for the model's intended use and level of risk.

- Evaluate AI-specific model risks, including model performance and reliability, bias and fairness, explainability, robustness, data quality, hallucination, drift, misuse, human oversight, and other risks associated with generative and agentic AI.

- Support the development, review, and ongoing maintenance of model documentation, including model development documentation, validation reports, model risk assessments, monitoring plans, limitations, assumptions, and supporting evidence.

- Maintain and support the enterprise model inventory to help ensure AI and machine learning models are appropriately identified, classified, risk-tiered, documented, and governed throughout their lifecycle.

- Partner with model owners and stakeholders to maintain accurate and complete model inventory records, including ownership, use cases, dependencies, risk classifications, validation status, monitoring requirements, and lifecycle status.

- Track model validation findings, limitations, remediation activities, exceptions, and other model risk issues and escalate significant concerns in accordance with established risk thresholds.

- Support ongoing model performance monitoring and periodic review activities, including assessment of model performance, stability, drift, changes in model use, and continued fitness for purpose.

- Develop and maintain model risk metrics, key risk indicators, inventory reporting, validation sta