What are the responsibilities and job description for the AI Solution Product Manager position at Purple Drive Technologies LLC?
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.