Senior AI Product Manager, HR Tech

Trinet

Lithonia (GA)

On-site

USD 90,000 - 168,000

Full time

6 days ago

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Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) plan

Employee stock purchase plan

Paid time off

Holidays

Job summary

TriNet is seeking a Senior AI Product Manager to lead strategy, development, and delivery of agentic AI experiences across HR domains such as Payroll, Benefits, Workforce Management, HR Operations, and Compliance. You will identify high-impact AI opportunities, define capabilities, and partner with cross-functional teams to deliver scalable, trusted experiences.

This is a strategic, execution-oriented role with a focus on applying AI to solve real problems, improve efficiency, and drive

Qualifications

5+ years direct product management experience, with 2+ years building AI solutions.
Experience owning products from strategy through launch in a cross-functional environment.
Experience in enterprise SaaS, HR technology, workflow automation, or customer-facing platforms.
Experience with AI-powered products, conversational experiences, or intelligent systems is required.
Strong analytical and problem-solving skills with a data-driven approach to decision making.
Excellent communication and stakeholder management skills across technical and non-technical teams.
Familiarity with HR domains such as Payroll, Benefits, WFM, HR Operations, or Compliance is a plus.

Responsibilities

Defines the strategy, roadmap, and owns execution of AI-powered product capabilities across HR domains such as Payroll, Benefits, Workforce Management, HR Operations, and Compliance.
Identify and prioritize high-value AI use cases based on data insights, customer needs, operational pain points, business impact, and technical feasibility.
Define product requirements for agentic experiences, including intent framing, user workflows, prompts, guardrails, data needs, and success metrics.
Define foundational AI platform capabilities such as prompt management, prompt chaining, context awareness, agent-to-agent orchestration, MCP server and knowledge/data pipelines.
Define evaluation frameworks and success criteria to measure AI behavior, accuracy, and overall product effectiveness.
Balance cost, quality, and latency tradeoffs to deliver performant, reliable, and scalable AI experiences.
Partner cross-functionally with domain product managers, engineering, data, UX, knowledge management, legal, and go-to-market teams to deliver scalable, compliant, and customer-centric AI experiences.
Drive end-to-end product delivery from discovery and design through launch, measurement, and continuous improvement.
Monitor product performance and business outcomes, using insights to optimize AI experiences, improve adoption, and inform roadmap decisions.