Sr Program Manager – AI/ML

Experience – 15+ yrs

Job Type – Remote

Job Description :

We are seeking a highly experienced Senior Program Manager to lead and evolve our enterprise-wide AI governance strategy. This role is critical in ensuring our AI systems are developed and deployed responsibly, ethically, and in compliance with global regulations—especially the EU AI Act. You will play a key role in reconciling governance processes following our recent acquisition of Juniper, aligning their AI governance practices with ours, and integrating them into our existing Architecture Review (ARV) workflows.

• 8+ years of experience in program management, risk, or compliance roles, with the background of AI or any kind of governance.
• 3+ Deep understanding of AI/ML technologies and lifecycle, including GenAI systems.
• 2 years of experience in ethical and responsible AI principles (accountability, explainability, fairness and transperancy)
• 2+ Proven experience with regulatory frameworks, especially the EU AI Act, NIST AI RMF, and OECD AI Principles.
• 5+ Strong background in post-merger integration and process reconciliation.
• Excellent communication, stakeholder management, and change leadership skills.

Key Responsibilities:

• AI Governance Leadership: Develop, implement, and maintain a robust AI governance framework aligned with regulatory, ethical, and business standards.
• EU AI Act Compliance: Lead readiness and compliance efforts, including risk classification, documentation, and conformity assessments.
• Post-Acquisition Integration: Reconcile Juniper’s AI governance processes with existing frameworks, identifying overlaps and harmonizing workflows.
• Architecture Review Alignment: Integrate AI governance reviews with the ARV process to ensure streamlined oversight and reduced duplication.
• Risk Management: Oversee AI risk assessments, model inventories, and validation processes across business units.
• Stakeholder Collaboration: Partner with legal, compliance, engineering, and product teams to embed governance into the AI/ML lifecycle.
• Training & Awareness: Develop and deliver training programs on responsible AI practices and regulatory requirements.
• Metrics & Reporting: Define KPIs and reporting mechanisms to measure governance effectiveness and drive continuous improvement.
• Enhancement of AI Intake Process and reconciliation with other processes (Agentic, ERAG, and existing workflows such as ARB).
• AI Use Case Inventory: Tracking status and implementing closed-loop reporting.
• AI Governance Mailbox Support.