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About This Role
About the Role
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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.
What You'll Do
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### 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
What We're Looking For
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### Must have
- 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 have
- Experience with AI\-native products, agents, LLMs, or automation platforms including hands\-on prompt/agent buildingBackground 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
Compensation
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Base salary: $160K–$200K
Equity: Meaningful ownership in a fast\-growing healthtech startup
About Hello Patient
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Hello Patient is transforming healthcare communication through advanced conversational AI. Our founding team spent 3\.5 years scaling Carbon Health's technology during hypergrowth, where we saw firsthand how much clinical time gets wasted on basic communication—great clinicians buried in admin work, patients stuck in phone tag.
Our AI platform proactively engages patients through perfectly timed calls and texts, resolving complex interactions end\-to\-end—from scheduling to billing to retention. Built with security and compliance at its core, our technology integrates with existing healthcare infrastructure to deliver 24/7 patient engagement.
Backed by Scale and 8VC, we're building the future of healthcare communication, one conversation at a time.
Compensation Range: $160K \- $200K
Salary Context
This $160K-$200K range is below the median for AI Agent Developer roles in our dataset (median: $212K across 45 roles with salary data).
View full AI Agent Developer salary data →Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 3,824 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Hello Patient, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $252,000 based on 90 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($180K) sits 29% below the category median. Disclosed range: $160K to $200K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Hello Patient AI Hiring
Hello Patient has 2 open AI roles right now. They're hiring across AI Agent Developer. Based in New York, NY, US. Compensation range: $200K - $230K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national median.
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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