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About This Role
Sr Product Manager for Agentic AI Team
Start your next chapter at Revecore! For over 25 years, we've been at the forefront of specialized claims management, helping healthcare providers recover meaningful revenue to enhance quality patient care in their communities. We're powered by people, driven by technology, and dedicated to our clients and employees.
As part of our team, you'll be rewarded with:
- Comprehensive medical, dental, vision, and life insurance benefits from the start of your employment
- 12 paid holidays and flexible paid time off
- 401(k) contributions
- Employee Resource Groups that build community
- Career growth opportunities
An excellent work/life balance
*
Location: Remote – USA
Position Summary
The Product Manager, Agentic AI is responsible for driving the definition, delivery, and evolution of Revecore's agentic AI capabilities, starting with Ops. This role partners closely with the Director of AI Engineering, AI Architect, and AI Developers, as well as Data Engineering and Data Science, to turn business problems into well\-scoped, phased agentic workflows. The Product Manager operates at the intersection of revenue cycle operations and emerging AI capability — translating ambiguous business needs into clear PRDs and a prioritized backlog, while ensuring the team grows its autonomy responsibly, one earned phase at a time. This role is critical to making sure Revecore's investment in agentic AI builds real trust with ops analysts and stakeholders, rather than outpacing it.
Key Responsibilities
- Own day\-to\-day backlog management for the Agentic AI team — intake, prioritization, and sequencing of work using the team's scoring framework.
- Drive the PRD process end\-to\-end: partner with the AI Architect, AI engineers, and data scientists to scope new agentic use cases, write and maintain PRDs, and shepherd them through review.
- Serve as the primary intake point for stakeholder requests, translating business needs into well\-scoped problems.
- Partner with the Director, AI Engineering on phase\-gate decisions — determining when a workflow is ready to advance from agent\-assisted to semi\-autonomous, and eventually to fully autonomous.
- Track and report on adoption, trust, success metrics, and revenue impact for each shipped workflow.
- Collaborate with Data Engineering and Data Science leads on shared initiatives, including data access and model choices, per the cross\-team collaboration model.
- Facilitate discussions to balance business priorities, technical feasibility, and responsible\-autonomy considerations across competing use cases.
- Translate ops workflows (claims, denials, remits) into a structured engineering backlog — complete with detailed requirements, acceptance criteria, and success metrics for each item.
- Help build the team's foundational processes and documentation as we go — planning cadence, discovery templates, and prioritization criteria.
- Participate in phase\-gate reviews to ensure workflows meet trust, accuracy, and adoption thresholds before advancing in within the AI engineering team.
- Maintain the team's PRD template and prioritization framework as living documents, refining them as the team learns.
- Coordinate deep discovery and refinement for new candidate applications and teams (e.g., Acclaim, Sales, Client Success) before committing to build.
- Represent the Agentic AI team in cross\-functional planning and stakeholder reviews.
This document is not an exhaustive list of all responsibilities, skills, duties, requirements, or working conditions associated with the job. Employees may be required to perform other job\-related duties as required by their supervisor, subject to reasonable accommodation.
Education/Licensing/Certifications
- Bachelor's degree in Business, Computer Science, Information Systems, or a related field; or equivalent work experience (required).
- Master's degree or advanced coursework in a technical or business discipline (preferred).
Work Experience \& Skills
- Experience translating ambiguous business objectives into clear, measurable outcomes and driving execution to deliver on them.
- Demonstrated experience in a product management role, ideally 3\-5 years, with a track record of owning a backlog and writing clear PRDs (required).
- Experience partnering closely with engineering and technical leads on scoping, architecture trade\-offs, and phased delivery (required).
- Strong stakeholder management skills, with experience translating business requests into well\-scoped technical problems (required).
- Proven ownership in building process and roadmap from scratch — driving accountability and outcomes without the benefit of an established playbook (required).
- Healthcare revenue cycle management (RCM) or claims/billing domain experience (preferred).
- Experience shipping AI/ML and agentic AI solutions from concept to delivery (preferred).
- Familiarity with AWS (Bedrock or similar) or working alongside a platform/infrastructure team (preferred).
- Background in ops/revenue\-cycle consulting, healthcare billing, or a related regulated industry (preferred).
- Strong analytical and problem\-solving skills, comfortable reasoning through ambiguous problems across business and technical domains.
- Excellent written and verbal communication skills, with the ability to translate effectively between technical and non\-technical audiences.
- Collaborative mindset with a partnership\-oriented approach and the ability to build trust across engineering and business stakeholder teams.
- Demonstrated resilience operating in a brand\-new, undocumented environment — building process and roadmap simultaneously, with no established playbook to fall back on.
- Attention to detail and a strong sense of ownership over the backlog, PRDs, and prioritization decisions.
- Ability to prioritize effectively and make well\-reasoned trade\-off decisions in a fast\-paced, ambiguous environment.
- Experience with Agile delivery practices including backlog management, sprint planning, and requirements definition.
Work at Home Requirements:
- A quiet, distraction\-free environment to work from in your home.
- A secure home internet connection with speeds \>20 Mbps for downloads and \>10 Mbps for uploads is required.
- The workspace area accommodates all workstation equipment and related materials and provides adequate surface area to be productive.
*Employment is contingent upon eligibility to work in the U.S., employment history verification, and a background check.*
*Revecore is an equal opportunity employer that does not discriminate based on race, color, religion, sex or gender, gender identity or expression, sexual orientation, national origin, age, disability status, veteran status, genetic information, or any other legally protected status. We believe that a diverse workforce fosters innovation and creativity, enriches our culture, and enables us to better serve the needs of our clients and communities. We welcome and encourage individuals of all backgrounds, perspectives, and abilities to apply.*
*Must reside in the United States* *within one of the states listed below:*
*Alabama, Arkansas, Florida, Georgia, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Massachusetts Michigan, Minnesota, Mississippi Missouri, Nebraska, New Hampshire North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Vermont, Virginia, West Virginia and Wisconsin*
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Revecore, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $217,100 based on 471 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Revecore AI Hiring
Revecore has 1 open AI role right now. They're hiring across AI Product Manager. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
Frequently Asked Questions
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