Technical Product Manager - AI
New
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SupplyHouse.comE-commerce HVAC plumbing
This remote position is open to individuals who live in, or are open to relocating to, the following states: Arizona, Delaware, Florida, Georgia, Kentucky, Nevada, New Jersey, New York, North Carolina, Ohio, Rhode Island, South Carolina, Tennessee, Texas, Virginia, and Washington., Monday through Friday, 8:00 a.m. to 5:00 p.m. with time zone flexibilityFull-TimeSenior
Salary$116,984 – $146,230 per year
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Job Details
- Experience
- 5–8 years
- Required Skills
- Artificial IntelligenceData AnalysisMachine LearningProduct ManagementJiraConfluencePrompt EngineeringLLM
Requirements
- Bachelor's degree in Computer Science, Data Science, Engineering, or a related technical field.
- 5–8 years of product management experience.
- At least 2 years leading AI/ML or data-driven product initiatives.
- Strong working knowledge of large language models, prompt engineering, RAG, agentic workflows, and recommendation systems.
- Demonstrated experience partnering with data science and ML engineering teams.
- Technical fluency in API documentation, data schemas, and model architecture tradeoffs.
- Proficiency with JIRA, Confluence, and agile/scrum methodologies.
- Strong analytical skills, including experience designing experiments and interpreting results.
- Exceptional communication and stakeholder management skills.
Responsibilities
- Define and own the AI product roadmap, spanning customer-facing features and internal tools.
- Partner with developers to scope, architect, and deliver AI solutions, from proof of concept through production deployment.
- Lead discovery by identifying high-value AI opportunities through engagement with customers, business teams, and data.
- Write detailed requirements, user stories, and acceptance criteria in JIRA and Confluence.
- Design and manage experiments and A/B tests to evaluate AI feature performance.
- Act as a translator between business stakeholders and technical teams.
- Monitor AI product performance post-launch by defining and tracking relevant metrics.
- Champion responsible AI practices, including data privacy, model fairness, and security.
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