AI Agent Product Manager

Hello Patient•Austin, TX

18d

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. We build AI-powered voice, sms, and chat agents that integrate deeply into how healthcare practices actually operate. Every new customer deployment is a product launch—new workflows, new edge cases, new integration requirements, new ways the agent needs to behave in the real world. This role exists to own that launch from scoping through go-live. You’ll also own the customer-facing roadmap for each deployment—defining requirements, sequencing what gets built, and aligning stakeholders on tradeoffs. This role combines product ownership with hands-on agent building — translating requirements into shipped agent behavior. You’ll gather requirements directly from customers, translate them into agent logic and workflows, and implement that behavior hands-on using prompts, guardrails, tools, and evals. You’ll design Mia’s behavior, ship production-ready multi-agent workflows, and turn real-world healthcare complexity into systems that actually hold up on live calls. This is a rare seat: you’ll get direct exposure to frontier LLM failure modes in production, and you’ll continuously iterate on agent performance using structured evaluation, testing, and real customer feedback.

Requirements

3+ years in Product Management or a closely related role (e.g., technical program management at a product-led company, founder/early employee wearing the PM hat) or a technical background (software engineer / forward-deployed engineer) who has operated in a PM-like capacity
Strong technical product background—comfortable with APIs, integrations, and understanding how systems connect
Experience shipping in ambiguous, fast-moving environments where the playbook doesn't exist yet
Proven ability to work across functions through influence, not authority
Bias toward ownership and action—you'd rather make a call and course-correct than wait for clarity

Nice To Haves

Experience with AI-native products, agents, LLMs, or automation platforms including hands-on prompt/agent building
Background in healthcare, revenue cycle, or other legacy-system-heavy industries
You’ve spent time playing with bots — building them, breaking them, and tweaking conversations to make them 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 (IVR, voice assistants, chatbots, SMS flows) and the rationale behind key design decisions
Familiarity with evaluation practices for agent quality

Responsibilities

Own customer deployments end-to-end
Lead delivery of net-new AI agent deployments as the single accountable owner—from customer requirements through production go-live
Treat every deployment as a product launch: define scope, manage tradeoffs, hit timelines, ensure quality
Own the customer-facing roadmap for each deployment—what are we shipping, in what order, and why
Build agents, not just specs
Gather requirements directly from customers and translate them into agent behavior yourself using our internal platform—prompts, tools, guardrails, multi-agent workflows
Do hands-on prompt engineering using AI tools to build the components that make agents work: conversation flows, decision logic, edge case handling, integration behaviors
Ship production-ready agents that handle real healthcare complexity: scheduling logic, patient context, EMR data, practice-specific edge cases
Continuously improve agent performance using structured evals, live call review, and direct customer feedback
Drive zero-to-one execution
Lead deployments for new workflows, integrations, and verticals where no playbook exists
Identify unknowns early, derisk them, and make tradeoff decisions that keep launches on track
When deployments require net-new platform capabilities, own requirements and prioritization with the engineering team
Work alongside Conversational Design and Customer Engineering
Conversational designers design and optimize agent behavior—they review real conversations, identify patterns, design conversation flows, and ship agent behavior
Customer engineers build integrations into our customers' systems of record—EHRs, PMS, and PIMS
You own everything else: integration scoping with Customer Engineering, cross-team coordination, timeline management, stakeholder communication, and unblocking whatever's in the way
Build toward repeatability
Identify patterns across launches and codify them into playbooks
Know when a workflow or integration is ready to transition from zero-to-one ownership to steady-state implementation
Hand off proven patterns to implementation teams and move on to the next net-new challenge