AI Agent Product Manager, Builder

Hello Patient•New York, NY

About The Position

Hello Patient is hiring an AI Agent Product Manager to build AI voice agents from scratch for healthcare workflows. The role involves designing how agents listen, interpret, respond, and guide patients across voice, SMS, and chat, including conversation structure, language, tool use, transfers, and recovery from errors. This is a hands-on building role: you will create prompts, conversation flows, and agent configurations, then use simulations and real agent conversations to identify breakdowns and improve performance. You will partner with Engineering and Customer Delivery Product Managers to ensure agents work reliably in production. You will own agent architecture and configuration, translate customer processes and constraints into agent behavior, and build agents that handle healthcare complexity such as scheduling, patient context, EHR data, and practice-specific requirements. You will also own agent quality through evaluations, simulations, peer reviews, and production troubleshooting, and remain the architectural owner of agents after handoff. The role includes creating playbooks, preparing support teams, reviewing pull requests, leading architectural refactors, and developing reusable components and patterns across deployments. Hello Patient builds conversational AI for healthcare communication, including calls and texts that resolve patient interactions such as scheduling, billing, and retention. Its platform is designed to integrate with existing healthcare infrastructure, with security and compliance at its core.

Requirements

At least 1 year of experience in conversational design, voice user experience, or a closely related field, with hands-on experience building AI agents or conversational systems from scratch.
Experience working closely with voice AI agents, including designing, testing, and refining their behavior in real conversations.
Strong understanding of conversation structure, turn-taking, intent recognition, ambiguity, clarification, and recovery when interactions go off track.
Hands-on prompt engineering skills and ability to translate customer workflows into agent instructions, conversation flows, and decision logic.
Ability to review calls and transcripts, identify patterns in conversational failures, and turn findings into concrete improvements.
Technical fluency with agent-building platforms, tool calls, APIs, and integrations, including understanding how system constraints affect conversation design.
Strong ownership and ability to work across customer-facing and Engineering teams in a fast-moving environment.

Nice To Haves

Background in linguistics, computational linguistics, discourse analysis, human-computer interaction, or a related discipline.
Experience shipping voice AI agents into production and improving them using real call data.
Familiarity with speech recognition and text-to-speech systems, including the effects of latency, interruptions, pronunciation, and speech recognition errors on the experience.
Experience building evaluations and simulations for conversational quality and task completion.
Background in healthcare or other environments with complex, sensitive customer interactions.
A portfolio of voice or conversational experiences built, with examples of how testing informed design decisions.

Responsibilities

Design and build production-ready agents, owning their architecture and configuration, including prompts, intents, conversation flows, tool calls, extensions, transfer protocols, and retrieval-augmented knowledge bases.
Lead technical discovery to translate customer business processes, rules, exceptions, and handoffs into agent behavior.
Build agents that handle healthcare workflows, including scheduling logic, patient context, EHR data, and practice-specific requirements.
Partner with Engineering on integrations, new features, and end-to-end testing, translating integration constraints into agent design decisions.
Assess technical feasibility and help the Customer Delivery Product Manager understand implementation effort and tradeoffs before scope and timelines are committed.
Author evaluations, tags, and simulation scenarios that test expected behavior, edge cases, and failure modes.
Build and run simulation suites, review results, and own internal agent quality before customer demos and testing.
Peer-review other builders’ agents and take accountability for technical approvals.
Diagnose issues during soft launch and the initial period after go-live, using live conversations, evaluations, and customer feedback to guide improvements.
Decide whether issues need targeted fixes or agent re-architecture.
Author the QA Agent Playbook and prepare Agent Technician and QA teams to support agents after handoff.
Remain the architectural owner of agents built, advising on changes that affect their structure and behavior.
Review Agent Technician pull requests and lead refactors requiring deeper architectural work.
Assess whether expansion requests can reuse existing features or require new capabilities.
Turn patterns across deployments into reusable components and patterns so future agents can be built faster.