Note: The job is a remote job and is open to candidates in USA. CaseWorthy, Inc. is a unified whole-person care platform for human services, serving government and nonprofit organizations. The Senior Platform Product Manager, Data & AI will own the AI and data foundation of the platform, leading the product management for Cara and CaseWorthy CORE, ensuring that they meet the needs of application teams and improve caseworker efficiency.
Responsibilities
- 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
- 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
Skills
- 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
- 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
Company Overview