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Hellopatient

AI Agent Product Manager, Builder

New York, NYHybrid · United States
FullTime · Product & Operations
POSTED 4 NOV
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Role
5 days opentrue age
Posted 4 Nov
Payats field
$110k–145kposted
▼ −36% vs market median $200k
$110k$228k
Market p25–p75
$164k–$228k
New York · n=107 · 365d to 6 Nov
CompanyHellopatient →
10open roles
Opened · 30d
+0
Closed · 30d
−0
30d window
Market
3,993open · Product
Opened · 7d
+2526% of open
All product
8,137open
All tracked boards · live 10 Nov · n=3,993

About The Role

Hello Patient is hiring an AI Agent Product Manager with a strong background in building AI voice agents from scratch. We build AI-powered voice, SMS, and chat agents that integrate deeply into how healthcare practices actually operate.

You’ll design how our agents listen, interpret, respond, and guide patients through real healthcare workflows. From conversation structure and language choices to tool use, transfers, and recovery when something goes wrong, you’ll turn complex requirements into conversations that feel natural and accomplish the task.

This is a hands-on building role. You’ll create prompts, conversation flows, and agent configurations yourself, then use simulations and real chats with the agent to understand where conversations break down and improve them. You’ll work closely with Engineering and Customer Delivery Product Managers to make sure the experience holds up in production.

A background in conversational design, linguistics, voice user experience, or a related field is especially relevant. You should enjoy both the craft of conversation and the technical work of making an agent behave reliably.

What You'll Do

Design and build production-ready agents

Own agent 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 real healthcare complexity, 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 PM understand implementation effort and tradeoffs before scope and timelines are committed.

Own agent quality and production performance

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 the technical approvals you provide.

Diagnose issues during soft launch and the initial period after go-live, using live conversations, evaluations, and customer feedback to guide improvements.

Decide when an issue requires a targeted fix and when the agent needs to be re-architected.

Guide agents as they evolve

Author the QA Agent Playbook and prepare Agent Technician and QA teams to support each agent after handoff.

Remain the architectural owner of the agents you build, advising on changes that affect their structure and behavior.

Review Agent Technician pull requests and lead refactors that require 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 that future agents are built faster

What We're Looking For

Must have

1+ years 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 how they behave in real conversations.

Strong understanding of conversation structure, turn-taking, intent recognition, ambiguity, clarification, and recovery when an interaction goes off track.

Hands-on prompt engineering skills and the 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 those findings into concrete improvements.

Technical fluency with agent-building platforms, tool calls, APIs, and integrations, including how system constraints affect the conversation.

Source · HellopatientFirst seen 7 NovOriginal ↗