Head of AI Operating System

Raintree Systems, Inc•,

8d•$220,000 - $250,000•Remote

About The Position

This role is for a builder who can design, ship, and demonstrate measurable business impact from an internal, agentic AI Operating System. Raintree is creating a system that will run across every function of the business, automating workflows, enriching decision-support with frontier models, and integrating all systems of record. The semantic and data layer is foundational to this system. The ideal candidate will be a pioneer in operating a SaaS business as an AI-native company. Raintree is a leading enterprise SaaS platform for the physical, occupational, and speech therapy industries. The company is profitable, growing, and undergoing an AI-driven reinvention. Prior healthcare experience is not required; the ability to learn a business quickly and ship is essential.

Requirements

Top-tier academic background in a leading university and quantitative discipline (Computer Science, Engineering, Mathematics, Physics, Economics, or similar).
Roughly 6–10 years of progressive experience, ideally a combination of technical AI/ML work, top-tier strategy consulting (MBB) or operating role at a high-performance SaaS company, and at least one role where you built something from zero.
Currently in a senior IC or director-track role at a strong company, not yet in a Head of / VP seat where you have stopped touching the work.
Business mind first, AI specialist second; thinking in P&L, ROI, EBITDA, and customer NPS.
Ability to read and challenge a financial model.
Real, recent, hands-on expertise applying frontier models (Claude, GPT, Gemini, leading open source) in production.
Deep fluency with agentic workflows: planner-executor patterns, tool use, evaluation harnesses, observability, guardrails, human-in-the-loop design.
Working knowledge of MCP (Model Context Protocol) and a track record integrating LLM systems with Salesforce, ticketing, Slack, Gong, finance and close tooling, and similar SaaS surfaces.
Comfort designing and building semantic layers, retrieval architectures, and the data plumbing that makes agents reliable.
Practical understanding of AI security, privacy, and risk: prompt injection, data exfiltration, audit logging, red-teaming, and HIPAA/PHI handling.
Builder's bias; ships working software, not slide decks.
Comfortable moving between the boardroom and the IDE in the same day.
Allergic to AI theater; calibrated about what works and what is hype.
Operates with conviction and welcomes accountability.
Force multiplier — gets work done through a network of champions.
Organizational orchestrator — can align a network of senior stakeholders around a coherent program agenda without direct authority.

Nice To Haves

Graduate degree.
Healthcare experience.
Experience defining and shipping AI systems that have produced measurable business outcomes.
Experience building the Agentic AI Operating System of the next SaaS era.

Responsibilities

Lead at the ELT level, partnering with the CEO and ELT to define Raintree's internal AI strategy and operating priorities, defending the roadmap with rigorous ROI analysis.
Build and chair an executive AI council that sets policy, approves investments, and unblocks cross-functional work.
Run the AI champions network, a working group of departmental representatives, as the operating engine of the program.
Brief the board on AI program health, financial impact, and forward investment plan on a quarterly cadence.
Navigate cross-functional alignment without direct authority, earning credibility with C-level peers to move agendas.
Architect and ship agentic workflows on frontier models (Claude, GPT, Gemini, leading open source) that automate or substantially augment real work across the business outside of core engineering.
Build the semantic and data layer, including the production-grade context pipeline across Raintree’s systems of record, in partnership with engineering leadership.
Build MCP-based integrations into Salesforce, Slack, Gong, ticketing systems, finance and close tooling, and other internal SaaS.
Establish evaluation, observability, and red-team practices to keep agents performant and safe in production.
Prototype quickly, ship pragmatically, and migrate proven work to long-term owners without losing operational control.
Build Raintree's AI governance framework for evaluating, approving, procuring, deploying, monitoring, and retiring AI tools.
Define data-handling policy for HIPAA, PHI, and patient-data sensitivities, partnering with Security, Legal, and Compliance.
Contain shadow AI, rationalize the vendor portfolio, and negotiate enterprise contracts.
Stay ahead of the regulatory landscape (OCR/HIPAA AI guidance, state-level healthcare AI rules, EU AI Act spillover) and translate it into operating practice.
Stand up a dashboard showing AI spend, adoption, project status, validated EBITDA impact, and customer NPS movement.
Co-own ROI validation methodology with Finance, ensuring baseline and post-launch measurements for every initiative.
Hold initiatives accountable to committed outcomes and discontinue ineffective ones.