AI Solution Product Manager

Job Description
AI Solution Product/Program Manager

Must Have Technical/Functional Skills

• 8-10 years of Product or Solution Management experience.
• Experience ideating, implementing and monitoring product, governance frameworks, including defining and tracking KPIs
• Strong knowledge of SDLC and Technical acumen.
• Strong understanding of API integrations.
• Experienced to lead large scale program and establish new processes
• Strong analytical problem-solving skills and comfortable with ambiguity.
• Strong written and verbal communication skills. You're able to articulate the reasons behind your work and bring others along with you.
• Strong collaboration & relationship skills You will be the evangelist for the strategy and roadmap but won't be successful without bringing others along with you. Your ability to listen deeply, contextualize your insights are essential to achieving the best outcomes.
• Ability to thrive in a fast-paced environment, with competing resources and priorities.
• Hard-working, self-motivated, and strong willingness to learn and make an impact.

Roles & Responsibilities

• Creating and maintaining a framework that outlines the policies, processes, and procedures for solution development and management.
• Establish clear lines of accountability and decision making for solution development and management.
• Effectively communicate, influence, and collaborate with diverse stakeholders, including executives, and development teams to successfully drive governance enforcement.
• Perform solutioning duties by de-mystifying complex needs in partnership with Product Delivery, Technology and Design partners.
• Documenting and Socializing solution frameworks and standards with Product and Technology teams.
• Creating and presenting leadership updates.
• Identify, prioritize, and deliver AI/ML and Generative AI use cases that measurably improve acquisition funnel performance (e.g., conversion, completion rates, decision speed, personalization).
• Translate business challenges into AI-enabled product requirements in partnership with Data Science and Engineering teams.
• Define experimentation strategies (A/B testing, pilots, model performance metrics) to validate AI solutions and quantify business impact.
• Partner with Risk, Compliance, and Governance teams to ensure Responsible AI implementation and regulatory adherence.
• Embed model monitoring, feedback loops, and performance KPIs into product lifecycle management.
• Influence roadmap prioritization to transition EAPP toward AI-native platform capabilities.