Key Responsibilities:
Responsible AI Strategy & Framework Development
- Define and evolve the Responsible AI framework covering fairness, bias, explainability, robustness, privacy, safety, and regulatory compliance.
- Collaborate with AI engineering, data science, product, legal, security, compliance, and ethics teams to operationalize Responsible AI standards.
- Translate global Responsible AI principles and emerging regulations into company-specific policies, guardrails, and workflows.
- Identify innovative approaches to embed Responsible AI across design, development, deployment, and monitoring stages.
Product Ownership Responsible AI Tooling
- Act as Product Owner for the Responsible AI assessment and governance tool.
- Define product vision, roadmap, success metrics, and prioritization aligned with business and regulatory needs.
- Convert Responsible AI requirements into product features: risk assessments, checklists, metrics, dashboards, and approval workflows.
- Work with engineering teams to deliver end-to-end solutions from concept to production.
- Ensure usability, scalability, and adoption across predictive and generative AI teams.
Stakeholder Engagement & Enablement
- Lead workshops, reviews, and design discussions to guide teams in applying Responsible AI practices.
- Drive organization-wide awareness through training, documentation, and best-practice playbooks.
- Act as a trusted advisor to leadership on AI risk, ethics, and governance decisions.
Risk Assessment & Governance
- Establish AI risk classification and maturity models (low / medium / high risk).
- Oversee Responsible AI assessments across AI use cases, ensuring risks are identified, mitigated, and documented.
- Define monitoring and audit mechanisms for post-deployment AI systems.
- Track regulatory trends and ensure proactive alignment with global AI governance expectations.
Success Metrics:
- Responsible AI principles embedded into AI development lifecycle.
- High adoption of the Responsible AI tool across teams.
- Measurable reduction in AI risk exposure and improved compliance readiness.
- Strong trust and collaboration between AI teams and governance stakeholders.
