Head of AI Operating System
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Job Description
Head of AI Operating System at Raintree driving internal AI strategy and agentic workflows. Partnering with CEOs and ELT to ensure measurable business impact through AI initiatives.
Responsibilities:
- Partner with the CEO and ELT to define Raintree's internal AI strategy and operating priorities; defend 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
- Architect and ship agentic workflows on frontier models (Claude, GPT, Gemini, leading open source) that automate or substantially augment real work across the business
- Build the semantic and data layer that is the foundation of the entire program
- Build MCP-based integrations into Salesforce, Slack, Gong, our ticketing systems, our finance and close tooling
- Establish the evaluation, observability, and red-team practices that keep agents performant and safe in production
- Build Raintree's AI governance framework
- Track, validate, and report ROI
Requirements:
- Top-tier academic background — 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
- Business mind first, AI specialist second
- 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
Benefits:
- Comprehensive medical, dental, vision
- 401(k)
- Fully remote-first culture
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