Job Title

Product Owner – Responsible AI & AI Governance

 

Role Overview

We are seeking a seasoned Responsible AI leader with deep expertise in AI/ML systems and Responsible AI principles to define, build, and scale a company-wide Responsible AI framework. This role will act as a strategic partner to engineering, product, legal, compliance, and leadership teams, while also serving as the Product Owner for an internal Responsible AI assessment and governance tool.

The ideal candidate combines technical depth, strategic thinking, and hands-on product leadership to translate Responsible AI principles into practical, scalable, and measurable implementations across the organization.

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 cross-functional stakeholders (AI engineering, data science, product, legal, security, compliance, and ethics) to co-create and operationalize Responsible AI standards.

• Translate global Responsible AI principles and emerging regulations into company-specific policies, guardrails, and workflows.

• Continuously identify new, innovative, and high-impact ideas to embed Responsible AI into the AI development lifecycle (design → development → deployment → monitoring).

Product Ownership – Responsible AI Tooling

• Act as Product Owner for the company’s 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 clear product features (risk assessments, checklists, metrics, dashboards, approval workflows).

• Work closely with engineering teams to drive end-to-end delivery, from concept to production.

• Ensure usability, scalability, and adoption across diverse AI teams (predictive and generative AI).

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.

What Success Looks Like

• Responsible AI principles are embedded into the AI development lifecycle, not treated as a checklist.

• 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.

Required Qualifications

Technical & Domain Expertise

• Strong understanding of AI/ML systems, including both predictive and generative AI.

• Deep expertise in Responsible AI principles: fairness, bias mitigation, explainability, transparency, robustness, privacy, and safety.

• Hands-on experience evaluating AI risks in real-world production systems.

• Familiarity with Responsible AI tooling, metrics, and model evaluation techniques.

Product & Leadership Skills

• Proven experience as a Product Owner / Product Manager for internal or platform products.

• Ability to translate abstract governance principles into actionable product requirements.

• Strong stakeholder management and cross-functional leadership skills.

• Comfortable influencing without authority across engineering and leadership teams.

Experience & Education

• 6-10 years of experience in AI, data science, ML engineering, AI governance, or related fields.

• Experience designing or implementing enterprise-level AI governance or Responsible AI programs.

• Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related field (PhD a plus).

Desired / Nice to Have

• Experience in regulated industries (automotive, healthcare, finance, or similar).

• Exposure to global AI regulations and standards (e.g., ISO, NIST, EU AI Act concepts).

• Experience building AI risk assessment frameworks or governance platforms from scratch.

• Strong storytelling and communication skills to influence senior leadership.