AI Product Owner

Job Description

Forward Deployed AI Product Owner

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Education & Data Platforms

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Location: Remote - United States (Eastern time zone preferred)

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Role Summary

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A highly strategic and execution-oriented Forward Deployed AI Product Owner works directly with education customers, internal AI engineering teams, and domain experts to transform complex workflows into AI-native products.

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This role sits at the intersection of consulting, product management, AI, and customer success. The AI Product Owner will embed with customers, understand their business processes, identify opportunities for AI transformation, define product requirements, and lead delivery through an AI-first SDLC.

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This is a client-facing role where you will think like a consultant, act like a product leader, and execute like a startup founder. The ability to make critical decisions to adapt and drive the product forward is key.

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What You'll Do

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Customer Discovery & Consulting

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• Partner directly with education organizations, publishers, universities, K-12 districts, and learning companies.
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• Lead discovery workshops to understand business objectives, pain points, workflows, and data ecosystems.
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• Map current-state and future-state processes.
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• Translate ambiguous business problems into product opportunities.
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• Build trusted relationships with executive stakeholders.
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AI Product Ownership

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Own AI products from concept through deployment.

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Responsibilities include:

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• Define product vision and roadmap.
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• Create Product Requirement Documents (PRDs).
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• Write detailed user stories and acceptance criteria.
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• Prioritize product backlog.
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• Vibe coding to bring ideas to life
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• Lead sprint planning.
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• Manage releases.
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• Drive adoption and customer success.
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AI-First SDLC Leadership

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Lead products through an AI-native software development lifecycle, including:

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• Discovery
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• Requirements generation
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• AI-assisted architecture
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• Agent design
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• Human review workflows
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• Development
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• Testing
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• Evaluation
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• Deployment
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• Continuous optimization
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You will work closely with AI engineers to ensure products leverage:

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• LLMs
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• Agentic workflows
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• RAG
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• Knowledge Graphs
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• MCPs
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• AI orchestration
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• Multi-agent systems
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Education Domain Expertise

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Understand how learning products are created and consumed.

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Examples include:

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• Curriculum development
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• Instructional Design
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• Assessments
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• Accessibility
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• Learning standards
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• Learning Management Systems
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• Content Management Systems
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• Learning Objects
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• Metadata
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• Digital publishing
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• Course authoring
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Experience with publishers or EdTech organizations is highly desirable.

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Data Strategy

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Partner with customers to define AI-ready data foundations.

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Responsibilities include:

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• Data discovery
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• Metadata strategy
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• Taxonomy design
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• Ontology development
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• Knowledge repositories
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• Data quality
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• Data governance
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• Semantic search
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• Vector databases
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• Enterprise search
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• AI content repositories
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Responsibilities

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• Lead customer workshops
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• Convert business problems into AI solutions
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• Create product roadmaps
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• Write detailed PRDs
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• Prioritize product backlog
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• Manage Agile ceremonies
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• Define MVPs
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• Identify AI use cases
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• Define agent workflows
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• Create business process maps
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• Design future-state workflows
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• Drive product adoption
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• Define KPIs and success metrics
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• Coordinate releases
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Required Qualifications

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Experience

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• 8+ years in Product Management, Product Ownership, or Solution Architecture
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• Experience delivering enterprise software products
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• Experience working directly with customers
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• Experience in Agile environments
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• Experience with AI-enabled products
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• Experience within Education, Publishing, Learning Platforms, or EdTech
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Consulting Skills

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• Executive communication
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• Stakeholder management
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• Workshop facilitation
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• Process mapping
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• Requirements gathering
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• Business case development
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• Change management
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Ideal Candidate Profile

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The ideal candidate is not just a Product Owner. They are a Forward Deployed AI Product Owner who combines the strengths of a management consultant, product manager, AI strategist, and education domain expert. They thrive in ambiguity, can move seamlessly from an executive workshop to a backlog refinement session, understand both enterprise data architecture and learning ecosystems, and are passionate about building AI-native products that transform how education organizations operate.

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