Product Owner – AI LLM
Raleigh, WV - USA
Job Summary
Job Title: Product Owner AI / LLM
Location: Raleigh NC (Hybrid 2 days/week onsite)
Duration: Long Term Contract
Role Overview
We are looking for an experienced Product Owner with AI/LLM exposure who can bridge business needs and AI-driven product development. This role requires a strong foundation in product ownership along with the ability to contribute to AI initiatives including Generative AI and LLM-based solutions.
Key Responsibilities
Location: Raleigh NC (Hybrid 2 days/week onsite)
Duration: Long Term Contract
Role Overview
We are looking for an experienced Product Owner with AI/LLM exposure who can bridge business needs and AI-driven product development. This role requires a strong foundation in product ownership along with the ability to contribute to AI initiatives including Generative AI and LLM-based solutions.
Key Responsibilities
- Own and manage product roadmap backlog and prioritization.
- Work closely with stakeholders to translate business problems into AI-driven solutions.
- Lead product development efforts involving AI/LLM capabilities.
- Collaborate with engineering data science and ML teams to build scalable AI products.
- Drive Agile ceremonies and ensure timely delivery of product features.
- Communicate effectively with both technical and non-technical stakeholders.
- Support adoption governance and trust-building for AI solutions.
- Measure product success including value realization risk and performance.
- 7 10 years of total experience:
- Minimum 3 years as a Product Owner
- Prior experience as Business Analyst (or similar role)
- Strong experience in:
- Product roadmap creation & management
- Agile methodologies
- Hands-on or leadership experience in AI/LLM product development
- Ability to communicate AI concepts clearly to stakeholders
- Strong understanding of:
- Generative AI LLMs RAG NLP
- AI/ML lifecycle and ML Ops
- AI ethics and responsible AI practices
- Experience working with cross-functional teams (engineering data science)
- Machine Learning lifecycle & ML Ops (deployment monitoring maintenance)
- Generative AI & LLMs
- Retrieval-Augmented Generation (RAG)
- Natural Language Processing (NLP)
- Understanding of:
- Where AI adds value
- Risk measurement and mitigation
- Governance and compliance
- Adoption challenges and trust-building