Senior AI Product Manager, Agents
New
USFull-TimeSenior
Salary155,000 - 185,000 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLData AnalysisMachine LearningProduct Management
Requirements
- 5+ years of experience in product management, applied AI/ML, data products, or technical product roles with a strong emphasis on building intelligent systems.
- Proven experience building and deploying AI agents or automation systems beyond prototypes into production environments.
- Strong technical fluency, including comfort with SQL, Python, data analysis, and experimentation frameworks.
- Deep understanding of how to decompose complex workflows into modular, agent-executable tasks and systems.
- Experience working with cross-functional teams including engineering, data science, and operations in fast-paced environments.
- Strong systems thinking with the ability to design scalable, composable workflows and automation architectures.
- Demonstrated ability to measure product impact through data, experimentation, and ROI-driven decision-making.
- Excellent communication skills with the ability to align technical and non-technical stakeholders around complex AI initiatives.
- Hands-on familiarity with modern agentic tools and frameworks is strongly preferred.
Responsibilities
- Lead the design, development, and deployment of agentic systems that automate and orchestrate complex operational workflows across growth and client delivery functions.
- Identify high-impact operational processes and translate them into agent-ready workflows and modular execution systems.
- Build, prototype, and deploy AI agents that interact with data, tools, and business systems to automate real-world tasks.
- Develop reusable “starter kits” and orchestration frameworks to enable scalable adoption of agentic solutions across teams.
- Own end-to-end product execution for assigned workstreams, from opportunity discovery through delivery and iteration.
- Partner with data science, engineering, and business teams to integrate structured data, experiments, and business rules into agent workflows.
- Define success metrics, monitor performance, and measure the business impact and ROI of deployed AI systems.
- Continuously refine agent behavior, reliability, and orchestration patterns based on real-world performance and feedback.
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