Senior Program Manager – Enterprise AI
Los Alamitos, CA ( 3 days onsite per week)
12+ months contract
Position Summary
The Senior Program Manager – Enterprise AI will lead the planning, execution, governance, and adoption of enterprise AI initiatives. This role will coordinate cross-functional teams across business, technology, data, security, privacy, architecture, and external partners to move high-value AI opportunities from concept through production and measurable business outcomes.
The ideal candidate is an experienced enterprise program leader with practical knowledge of Generative AI, LLMs, RAG, AI agents, enterprise applications, data platforms, and Responsible AI.
Key Responsibilities
- Own the Enterprise AI roadmap, priorities, milestones, dependencies, risks, budgets, and business outcomes.
- Establish and manage a structured process for AI use-case intake, evaluation, prioritization, and portfolio management.
- Lead AI initiatives from discovery and proof-of-concept through pilot, production deployment, adoption, and continuous improvement.
- Coordinate programs across business functions including Sales, Customer Service, Finance, Operations, HR, Marketing, and IT.
- Partner with architecture, engineering, data, application, and infrastructure teams to develop realistic delivery plans.
- Establish AI governance, stage gates, documentation standards, and delivery processes.
- Coordinate Security, Privacy, Legal, Compliance, Responsible AI, and Architecture reviews.
- Manage dependencies across enterprise applications, data platforms, APIs, cloud services, AI platforms, and integrations.
- Define and track KPIs including adoption, productivity, quality, cycle-time improvement, cost savings, customer impact, and ROI.
- Lead executive steering committees, program reviews, prioritization, risk management, and status reporting.
- Manage technology vendors, implementation partners, and external resources.
- Drive organizational readiness, communications, training, and adoption of enterprise AI capabilities.
Required Qualifications
- Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, Business Administration, or related field, or equivalent relevant professional experience.
- 10+ years of technology program/project management experience, including complex enterprise transformation programs.
- Experience leading or significantly contributing to an enterprise AI, Generative AI, AI/ML, or advanced technology program.
- Experience delivering programs involving enterprise applications, cloud platforms, data, APIs, integrations, or digital transformation.
- Working knowledge of LLMs, RAG, AI agents/agentic workflows, prompt engineering, AI/ML lifecycle concepts, and AI governance.
- Experience coordinating Responsible AI, security, privacy, legal, compliance, and technology risk requirements.
- Strong experience developing roadmaps, business cases, KPIs, executive dashboards, and value-realization measures.
- Strong understanding of Agile and iterative product-delivery methodologies.
- Excellent stakeholder management, executive communication, facilitation, and vendor-management skills.
- Ability to translate technical AI concepts into clear business decisions and outcomes.
Preferred Qualifications
- Master's degree in Business, Technology, Engineering, Data Science, or related discipline.
- PMP, PgMP, SAFe, Agile/Scrum, Prosci, or equivalent certification.
- Experience with platforms such as Salesforce, SAP, Oracle, Snowflake, Microsoft Azure, AWS, Google Cloud, or comparable enterprise platforms.
- Experience with an Enterprise AI Center of Excellence (CoE) or AI enablement program.
- Experience developing AI use-case prioritization and value-realization frameworks.
- Familiarity with Responsible AI frameworks such as NIST AI RMF.
Location & Working Hours
- Remote; candidate must be physically located within the United States.
- Regular working hours are 8:30 AM–5:30 PM Pacific Time, with consistent availability during these hours.
Success Measures
Success will be measured by the ability to move high-value AI opportunities into production, deliver programs predictably, increase enterprise AI adoption, manage risk effectively, and demonstrate measurable business value from AI investments.
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