Core Duties and Responsibilities: - Develop and execute the enterprise AI enablement strategy aligned with business objectives.
- Partner with business leaders to identify, prioritize, and scale high-impact AI use cases.
- Establish reusable frameworks, tools, and best practices to accelerate AI adoption.
- Build and lead internal AI communities of practice to drive knowledge sharing and innovation.
- Oversee AI solution lifecycle from ideation through deployment and monitoring.
- Design and implement a comprehensive AI governance framework covering model risk, ethics, privacy, and compliance.
- Define policies and standards for responsible AI use, including fairness, transparency, and accountability.
- Establish model validation, monitoring, and audit processes.
- Ensure compliance with evolving AI regulations (e.g., EU AI Act, U.S. regulatory guidance) and internal policies.
- Partner with Legal and Security teams to manage AI-related risks and ensure compliance.
- Collaborate with data teams to ensure high-quality, secure, and compliant data usage.
- Define standards for model documentation, explainability, and reproducibility.
- Implement processes for model performance tracking, drift detection, and retraining.
- Ensure proper governance over third-party AI tools and vendors.
- Build, mentor, and lead a high-performing AI governance and enablement team.
- Serve as a trusted advisor to senior leadership on AI opportunities and risks.
- Drive change management efforts to embed responsible AI practices across the organization.
- Deliver training and awareness programs on AI governance and ethical use.
- Evaluate and select AI platforms, tools, and vendors aligned with governance standards.
- Oversee implementation of AI governance tooling (e.g., model registries, monitoring systems).
- Ensure integration of governance controls into MLOps and data pipelines.
- 2 onsite days per week is an essential function of this position
- Additional duties as needed
Position Requirements: - Bachelor’s degree in Computer Science, Data Science, Engineering, or related field (Master’s preferred).
- 10 years of experience in AI/ML, data science, or related domains, with leadership experience.
- Proven experience developing and implementing AI governance frameworks.
- Strong understanding of machine learning lifecycle, model risk management, and data governance.
- Familiarity with AI regulations, ethical frameworks, and industry standards.
- Experience working cross-functionally with technical and non-technical stakeholders.
- Experience in regulated industries preferred (e.g., finance, healthcare, insurance).
- Knowledge of frameworks such as NIST AI Risk Management Framework, ISO/IEC AI standards.
- Experience with MLOps platforms, model monitoring tools, and AI lifecycle management.
- Strong communication skills with the ability to influence executive stakeholders.
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