Senior Product Owner – Research & Ontology

Overview of the position

The Senior Product Owner (SPO) – Research & Ontology is responsible for strategically managing the development of AI research initiatives, semantic search capabilities, ontology and metadata frameworks, and AI-powered product features that support our Enhesa AI products. You will work closely with other product owners, product managers, engineers, researchers, ontology specialists, designers and other stakeholders to ensure that innovative ideas are validated and transformed into scalable solutions. You will have a deep understanding of the technical and functional aspects of the product; this means knowing how it works, what makes it unique, and how it fits into the larger picture of the user’s needs and company’s overall strategy.

Main tasks and responsibilities

• Engaging with Product Managers, Engineering, Architecture, AI teams and business stakeholders to execute the vision, roadmap, and strategic direction of Enhesa’s AI research initiatives, ontology capabilities, and AI-powered products
• Defining and prioritizing AI research initiatives, semantic search capabilities, ontology improvements, metadata enrichment strategies, and AI-powered customer experiences that support strategic AI initiatives across the portfolio.
• Contributing to the definition and execution of the strategy, roadmap, and priorities for semantic search, retrieval-augmented generation (RAG), knowledge discovery, ontology evolution, metadata management, and AI-enabled content experiences.
• Ensuring AI research initiatives, ontology models, metadata frameworks, and AI-powered functionality align with governance, security, privacy, regulatory, explainability, and customer requirements.
• Working closely with Architecture, Engineering and AI teams to define system interactions, knowledge retrieval approaches, metadata standards, ontology structures, and AI solution capabilities.
• Coordinate delivery across multiple products, ensuring dependencies, ownership transitions, and delivery handoffs are clearly defined and managed.
• Facilitating the transition of initiatives from research, experimentation, and prototyping into product delivery, ensuring success criteria, business value, ownership, and delivery expectations are clearly understood before implementation begins.
• Ensuring teams have sufficient customer context, business understanding, user workflows, prototypes, and solution objectives to make informed product and technical decisions throughout the product lifecycle.
• Coordinating with architecture, engineering managers, tech leads, researchers, and engineers to ensure a common understanding of requirements, priorities, technical constraints, and business objectives.
• Strategically prioritizing complex AI and ontology-related backlog items based on business value, customer impact, technical dependencies, risk, and delivery sequencing
• Creating user stories and acceptance criteria that clearly articulate product requirements, expected behaviors, customer outcomes, and quality expectations for each feature or capability.
• Ensuring that maximum business value is delivered as early as possible, through effective planning, prioritization, and stakeholder alignment.
• Partnering with Product Management, UX, and business stakeholders to validate customer workflows, AI adoption patterns, usability expectations, and future product opportunities.
• Communicating with stakeholders to ensure alignment on priorities, functionality, delivery status, risks, dependencies, roadblocks, and strategic objectives
• Coaching Product Owners within the team, sharing experiences, best practices, and expertise to support professional growth and product management excellence

Key requirements

Education Level

Bachelor’s or advanced degree in Computer Science, Information Technology, Software Engineering, or related.

Suggested Experience

At least 3-5 years of experience as a Product Owner

Technical Skills

• Experience in delivering cloud-based software projects
• Strong knowledge of Agile methodologies, prioritization frameworks and implementation experience
• Knowledge of distributed systems, APIs, microservices, and modern software architectures, with the ability to understand how AI, search, ontology, and data capabilities integrate within larger product ecosystems.
• Experience working with data platforms, metadata management, information retrieval systems, and interoperability between systems.
• Knowledge of ontology management, taxonomy design, knowledge representation, semantic technologies, metadata frameworks, or information architecture concepts.
• Experience with semantic search, Retrieval-Augmented Generation (RAG), vector databases, knowledge discovery, and AI-powered user experiences.
• Experience collaborating with Research, Architecture, Engineering, Data, and Platform teams to translate technical designs and research outcomes into product requirements, roadmaps, and customer-facing capabilities.
• Familiarity with AI-enabled solutions, Large Language Models (LLMs), retrieval-based systems, AI evaluation approaches, and AI governance concepts.
• Experience managing the transition of initiatives from research and experimentation into scalable product capabilities is desirable.
• Knowledge of Jira, Confluence, and other product management and collaboration tools.

Soft Skills

• Communication skills & Stakeholder Management: Communicate with many different stakeholders, including members of Product Management, Research, Engineering, Data, Architecture, business teams, and internal customers. You need to communicate effectively in written form, verbally, and through visual means, translating complex AI, ontology, metadata, and technical concepts into language that is easily understood by stakeholders.
• Cross-Team Collaboration & Influence: Successfully align and drive outcomes across multiple teams and stakeholders without direct authority. Facilitates collaboration, resolves conflicts, and promotes shared ownership of objectives and results.
  Systems Thinking: Ability to understand and evaluate complex ecosystems of products, knowledge domains, ontologies, metadata frameworks, AI capabilities, content workflows, and organizational dependencies, balancing immediate delivery needs with long-term scalability, maintainability, discoverability, and business objectives
• Risk and Dependency management: Capacity to anticipate, assess, and address potential challenges, uncertainties or dependencies effectively. It requires critical thinking, adaptability, and good communication to navigate risks and make informed decisions, all while minimizing negative impacts and maximizing opportunities.
• Technical Expertise: Demonstrates a solid understanding of AI-enabled solutions, Large Language Models (LLMs), semantic search, Retrieval-Augmented Generation (RAG), ontology and metadata concepts, knowledge discovery, cloud technologies, and modern software development practices. Uses technical knowledge to make informed product decisions and facilitate discussions between business, research, and engineering teams.
• Research-to-Delivery Leadership: Experienced in guiding initiatives from discovery, experimentation, and research through validation, productization, and delivery. Facilitates smooth ownership transitions between Research, Ontology, and Delivery while maintaining focus on customer outcomes and business value.
• Problem-solving skills. Ability to solve complex problems and make decisions that benefit both the business and the customer. Identifies challenges, evaluates possible solutions, and balances customer needs, business value, technical feasibility, and strategic priorities to achieve meaningful outcomes.
• Strategic Execution & Prioritization: Successfully translate strategic objectives into actionable priorities and deliverables, balancing business value, technical complexity, dependencies, and risk to maximize value delivery.
• Leadership and coaching: Coach junior Product Owners, to help them grow in a safe environment, supported by the best practices in the market