AI Governance Product Manager | AI Governance Lead

Key Roles & Responsibilities

1. Product Strategy & Vision

• Define and lead the product roadmap for AI governance tools or platforms.
• Align product vision with evolving global AI regulations (e.g., EU AI Act, US Executive Orders).
• Drive prioritization based on risk severity, ethical considerations, and business impact.

2. Risk and Compliance Understanding

• Deeply understand AI/ML risks: data bias, model drift, explainability issues, hallucinations, and misuse.
• Translate these risks into actionable governance features such as audit trails, bias detection modules, consent frameworks, etc.
• Stay updated on regulatory, ethical, and privacy-related developments in AI.

3. Cross-Functional Collaboration

• Work with data scientists, engineers, legal/compliance, and ethicists to define responsible AI practices.
• Coordinate with InfoSec, DevOps, and Data Governance teams to embed AI checks across the MLOps lifecycle.

4. Governance Framework Development

• Design and integrate policy management, model documentation (Model Cards), and access control features.
• Support lineage and traceability of AI/ML models – from data ingestion to model deployment and retirement.

5. Stakeholder Engagement & Communication

• Act as a bridge between technical teams and leadership.
• Present governance KPIs, risk metrics, and audit findings to senior stakeholders.
• Train internal users and clients on using the AI governance platform effectively.

6. Platform Features & Capabilities

• Build and manage capabilities like:
• Fairness & bias analysis dashboards
• Risk-scoring engines for AI use cases
• Model Explainability (XAI) integration (e.g., SHAP, LIME)
• Red teaming interface for adversarial testing
• Regulatory compliance checklist tools

Required Skills and Qualifications

Technical & Domain Knowledge

• Solid understanding of AI/ML lifecycle, MLOps, and data pipelines.
• Familiarity with LLMs, computer vision, NLP, or tabular AI use cases.
• Strong grasp of AI-related risks and mitigation strategies.

Governance & Risk Awareness

• Exposure to responsible AI principles, ethical AI design, and risk governance frameworks.
• Familiarity with NIST AI Risk Management Framework, ISO standards, and emerging laws (like EU AI Act).

Soft Skills

• Strategic thinking and stakeholder management.
• Strong documentation and communication skills.
• Ability to translate complex AI issues into clear, business-relevant decisions.

Optional but Valuable

• Experience with AI red-teaming, privacy-preserving ML (e.g., differential privacy, federated learning).
• Experience using or building AI monitoring tools like Fiddler, Arthur, WhyLabs, TruEra, or Azure Responsible AI.
• Background in data privacy laws (GDPR, HIPAA, etc.).