Senior Manager, AI Program Management

New

Ability to work remotely from an eligible U.S. state.Full-TimeManager

SalaryExpected total annual compensation of $130,000–$190,000, including base salary and variable compensation, depending on location and experience.

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Job Details

Experience: 8+ years of experience in program management, product management, transformation, portfolio management, or a related discipline, including ownership of complex cross-functional technology programs.
Required Skills: AWSSalesforceGenerative AI

Have 8+ years of experience in program management, product management, transformation, portfolio management, or a related discipline.
Have owned complex cross-functional technology programs.
Bring meaningful experience delivering AI, data, analytics, cloud-platform, automation, or enterprise-software initiatives.
Have experience building or significantly improving a PMO, program-management practice, product operations function, or portfolio-management model.
Be able to turn ambiguity into structured, practical plans without unnecessary bureaucracy.
Have experience working with technical teams and understanding data, integrations, cloud infrastructure, security, software delivery, and production operations.
Be able to frame decisions and tradeoffs clearly for technical and non-technical audiences.
Bring a business orientation focused on adoption and measurable value from AI initiatives.
Be willing to personally create plans, develop trackers, manage dependencies, and resolve blockers.
Be able to work effectively in a lean, evolving environment where priorities can change and new structures must be created.
Direct generative AI or agentic AI experience is strongly preferred.
Familiarity with AWS, enterprise data platforms, Salesforce, Microsoft 365, Jira/Confluence, and modern AI or agent architectures is preferred.

Build the AI program-management function, including standards, operating rhythms, templates, portfolio reporting, intake, prioritization, decision tracking, and delivery governance.
Translate AI strategy into an actionable roadmap covering platforms, agent products, governance, observability, adoption, and business use cases.
Turn loosely defined opportunities into structured program charters with scope, ownership, dependencies, milestones, success metrics, and definitions of done.
Maintain a portfolio view of AI initiatives and make investment, capacity, dependency, and prioritization tradeoffs visible to leadership.
Lead end-to-end delivery of complex AI programs across engineering, data, security, legal, enterprise architecture, product, and business teams.
Establish a repeatable AI delivery lifecycle from discovery and data readiness through pilot development, evaluation, production launch, adoption, and ongoing measurement.
Prepare executive communications covering progress, business value, risks, decisions required, and next steps.
Establish measurement standards for adoption, quality, reliability, cost, risk, and realized business value.
Mentor future program and project managers as the function expands.

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