AI Agent Product Manager

Urban Ridge Supplies•New York, NY

8d•$160,000 - $200,000

About The Position

Hello Patient is hiring a technical, high-agency AI Agent Product Manager to own the end-to-end delivery of AI agents in real customer environments. The company builds AI-powered voice, SMS, and chat agents that integrate into healthcare practices. Each customer deployment involves new workflows, edge cases, integration requirements, and real-world agent behaviors. The role owns deployments from scoping through production go-live, as well as the customer-facing roadmap for each deployment, including requirements, sequencing, and stakeholder alignment on tradeoffs. This role combines product ownership with hands-on agent building. The Product Manager gathers requirements directly from customers, translates them into agent logic and workflows, and implements behavior using prompts, guardrails, tools, and evaluations. They design Mia’s behavior, ship production-ready multi-agent workflows, and iteratively improve agent performance through structured evaluation, testing, live-call review, and customer feedback. The role also leads zero-to-one deployments for new workflows, integrations, and verticals; coordinates with engineering and customer-facing teams; and identifies patterns that can be codified into playbooks and handed off to implementation teams. Hello Patient is a healthcare conversational AI company whose platform engages patients through calls and texts, handling interactions such as scheduling, billing, and retention, while integrating with existing healthcare infrastructure.

Requirements

At least 3 years of Product Management experience or experience in a closely related role, such as technical program management at a product-led company or founder/early-employee work performing PM responsibilities; alternatively, a technical background such as software engineering or forward-deployed engineering with PM-like experience.
Strong technical product background and comfort with APIs, integrations, and understanding how systems connect.
Experience shipping in ambiguous, fast-moving environments without an established playbook.
Ability to work across functions through influence rather than authority.
A bias toward ownership and action, including making decisions and course-correcting rather than waiting for clarity.

Nice To Haves

Experience with AI-native products, agents, LLMs, or automation platforms, including hands-on prompt or agent building.
Background in healthcare, revenue cycle, or other industries with legacy systems.
Experience building, breaking, or refining bots and making conversations feel more natural.
Founder, co-founder, or early startup experience.
Familiarity with voice AI or real-time orchestration systems.
Portfolio or examples of shipped conversational experiences, such as IVR, voice assistants, chatbots, or SMS flows, and the reasoning behind key design decisions.
Familiarity with evaluation practices for agent quality.

Responsibilities

Lead delivery of new AI agent deployments from customer requirements through production go-live as the single accountable owner.
Define deployment scope, manage tradeoffs, meet timelines, and ensure quality.
Own each deployment’s customer-facing roadmap, including what is shipped, in what order, and why.
Gather requirements directly from customers and translate them into agent behavior using the internal platform, including prompts, tools, guardrails, and multi-agent workflows.
Use AI tools for hands-on prompt engineering to build conversation flows, decision logic, edge-case handling, and integration behaviors.
Ship production-ready agents that address healthcare workflows such as scheduling, patient context, EMR data, and practice-specific edge cases.
Improve agent performance through structured evaluations, live-call review, and direct customer feedback.
Lead deployments for new workflows, integrations, and verticals where no established playbook exists.
Identify unknowns early, reduce risks, and make tradeoff decisions that keep launches on track.
Own requirements and prioritization with engineering when deployments require new platform capabilities.
Scope integrations with Customer Engineering and coordinate teams, timelines, stakeholders, and blockers.
Identify patterns across launches and codify them into playbooks.
Determine when workflows or integrations can move from zero-to-one ownership to steady-state implementation.
Hand off proven patterns to implementation teams and move on to new challenges.