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About This Role
Description:
The opportunity
CaseWorthy is the unified whole\-person care platform for human services. Roughly 1,000 government and nonprofit organizations run their programs on it, and the millions of people those programs serve depend on the data and decisions that move through it every day. Two things make that platform hard to replicate: CaseWorthy CORE, our unified data foundation, and Cara, the AI grounded natively in it and acting through the applications.
This role owns both — as products. Not features buried inside an application screen. The foundation layer every CaseWorthy application is built on. The person in this seat decides how AI shows up across the entire platform, sets the standard for how it is designed and trusted, and turns a one\-of\-a\-kind data foundation into intelligence that gives caseworkers time back. If you want to lead the pod that owns the data and AI foundation of a mission\-driven platform end to end — the infrastructure, the design, and the roadmap — this is that job.
Our governing principle is non\-negotiable: Cara recommends; humans decide. Every Assistant we design amplifies professional judgment. It never replaces it.
What you’ll own
Two connected pillars at the platform layer:
Cara — the AI. You own Cara as a product: its infrastructure, its family of Assistants, and its roadmap from reactive assistance to guided workflows to agentic automation. This is the majority of the role and where we most need a strong owner. You are the accountable product owner for every Assistant that ships — application teams build against your specs and standards.
CaseWorthy CORE — the data foundation. You own the unified data foundation: the lakehouse, the semantic models, and the cross\-program data layer that powers reporting, analytics, and every Cara interaction. CORE is read\-only by design — the single source of truth Cara is grounded in.
What you will not own is the application\-side feature work — how these capabilities surface inside ClientTrack, MediSked, and ServTracker. Application product managers own that. You build the foundation and the Assistants they consume, and you define the patterns; they light them up in context. Getting that boundary right — a strong platform layer that application teams extend by configuration, not one\-off forks — is central to the role.
What you’ll do
- Lead the Platform Pod. Set direction and own the operating rhythm for the pod that delivers the platform layer — the Cara, CORE, and Platform Engineering teams — driving its product, engineering, and design work to outcomes. You lead the product managers within the pod: define the PM standards, rituals, and ways of working, anchored in our Agentic Development Lifecycle (ADLC).
- Own the Cara roadmap across all three phases — reactive, guided, agentic — and the sequencing that earns trust before it expands capability.
- Design the Assistants. Write the specs: the job each Assistant does, its inputs and grounding, its autonomy settings (where a human allows, approves, or pre\-authorizes an action), its acceptance criteria, and its guardrails. Every spec anchors to “Cara recommends; humans decide.”
- Drive the infrastructure conversation, in partnership. Cara engineering owns the technical infrastructure decisions — model orchestration and routing, retrieval and knowledge grounding across the CaseWorthy University knowledge base, and the evolution from templatized to dynamic querying. You bring the product and cost lens and drive the decisioning alongside them.
- Own the MCP and API contract standards. Application teams build and own their MCP servers; you define the shared contract they implement — tool schemas, auth and permission scoping, consent, and the write\-boundary rules that keep “Cara recommends; humans decide” intact when agents act through the applications.
- Own the unit economics. Partner with Cloud \& Data Engineering on a fully\-loaded cost\-per\-use model and design the usage guardrails that keep AI durable at scale. That same cost number both prices Cara and scores what we build next — you own it as a product input.
- Set the responsible\-AI bar. Define the evaluation, safety, explainability, and human\-in\-the\-loop standards every Assistant clears before it ships — and hold the line on them.
- Own CORE as a product — the data foundation, semantic models, ingestion, and the analytics substrate Cara queries, including its role in statewide data\-infrastructure engagements. Protect the read\-only discipline of the foundation.
- Build design patterns that scale. Define reusable Assistant and data patterns that application teams extend by configuration across programs and verticals — build once, scale by configuration.
- Partner across engineering. Work with the Engineering organization on the agentic development lifecycle and with the AI Center of Excellence on shared standards.
- Prioritize in the open. Run your roadmap through the product prioritization framework, with runtime cost as a first\-class input for AI work, and make your decisions visible.
- Support go\-to\-market. Inform pricing and packaging for AI with the cost model and readiness signals — without owning the commercial motion.
Requirements:
What success looks like in your first year
- A repeatable Assistant design\-and\-evaluation standard exists, is documented, and is used by every application team shipping AI.
- CORE is the undisputed foundation for analytics and AI across the platform, and is ready to carry statewide data\-infrastructure engagements.
- The first guided\-phase Assistants are specified, in build, and on a credible path — with human\-in\-the\-loop and cost discipline built in from the start.
What you bring
- 6\+ years in product management, with meaningful time in platform product management and/or AI/ML product roles. You have owned a product that other teams build on.
- Hands\-on AI/ML product experience shipped to production — large language models, retrieval\-augmented generation, agentic systems, evaluation, prompt and context design, and model orchestration. You have shipped AI to real users, not just prototyped it.
- Data platform fluency — lakehouses, semantic models, and analytics. Familiarity with a modern data stack (Microsoft Fabric and Power BI a plus).
- Unit\-economics literacy — you can reason about and manage the cost of AI (cost\-per\-use, token economics, COGS) and design guardrails that keep it sustainable.
- A platform mindset — you think in contracts, reusable patterns, and configuration over forking, and you treat internal application teams as your customers.
- Exceptional spec\-writing and prioritization — you turn ambiguity into crisp, testable requirements and defensible sequencing.
- Experience leading product managers — setting standards, coaching, and running the rituals that make a small PM team effective, whether as a formal manager or a pod/team lead.
- A clear point of view on responsible AI — safety, guardrails, explainability, and keeping humans in the loop.
Nice to have
- Human services, govtech, healthcare, or another regulated enterprise SaaS domain.
- Experience participating in an AI FinOps or AI evaluation / quality function.
- Experience with data sovereignty and multi\-tenant data foundations.
Salary Context
This $100K-$150K range is in the lower quartile for AI Product Manager roles in our dataset (median: $188K across 140 roles with salary data).
View full AI Product Manager salary data →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 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At CaseWorthy, 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 $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($125K) sits 42% below the category median. Disclosed range: $100K to $150K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
CaseWorthy AI Hiring
CaseWorthy has 1 open AI role right now. They're hiring across AI Product Manager. Based in US. Compensation range: $150K - $150K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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