Senior Program Manager, AI Delivery

Key Responsibilities

• Manages end-to-end AI/ML project delivery, from planning through deployment and ongoing operations and monitoring.
• Develops detailed plans, sprint schedules, and coordinates resource allocations, ensuring on-time, on-budget, and high-quality execution.
• Applies agile methodologies, MLOps, and CI/CD best practices for streamlined AI/ML development.
• Adheres to IT Portfolio Strategy, Enterprise Architecture and Data Governance standards, collaborates with IT on vendor selection.
• Cross-functional Team Leadership & Collaboration: Leads, coordinates project resources, and enables high-performing project teams comprised of internal and external contract resources including ML and IT resources.
• Communicates effectively across disciplines, bridging business intent with technical execution, and transparent reporting on delivery, risks, and quality.

Experience & Qualifications

• Education: Bachelor’s degree in Information Systems, Computer Science, Engineering or related field with 7+ years of experience in program management, technical product management, AI or analytics delivery, engineering or equivalent experience.
• Professional Experience: Experience in product/program management with direct AI/ML solution delivery, track record of success and demonstrated ability to manage complex delivery programs across data and engineering teams.
• Technical Fluency: Hands-on experience implementing MLOps delivery controls including CI/CD pipelines for AI/ML solutions, strong conceptual understanding of machine learning, NLP, and emerging agent-based AI patterns sufficient to lead delivery and partner effectively with engineering teams.
• Familiarity: Familiarity with global privacy, access control, and regulatory compliance.

Capabilities & Leadership Attributes

• Stakeholder Management & Business Alignment: Communicates and establishes strong relationships with business and product stakeholders to ensure strong strategic alignment, defines clear success metrics and monitors KPI performance and value realization to inform continuous improvement, supports business case development and definition of scope & product requirements ensure AI solutions meet business needs, supports adoption and user enablement for delivered AI solutions, ensuring measurable business impact.
• Risk Management & Quality Assurance: Identifies, assesses, and mitigates AI project risks across technical, operational, ethical, and compliance domains, ensures adherence to quality standards, best practices, and regulatory compliance for AI solutions, implements governance mechanisms for ethical AI development, balancing innovation with control.