Role Overview
- Build Internal AI Agent Teams
- Design and deploy AI agents that handle operational work across portfolio companies — including customer support (Tier 1 & 2), SDR and sales outreach, onboarding automation, content workflows, and engineering maintenance
- Automate core business processes: CRM updates, document processing, reporting, and analytics pipelines
- Work directly with portfolio company operators to identify the highest-value automation opportunities
- Measure impact in hours of labor replaced, team output increased, or cost savings generated
Build External AI Products
- Develop new AI-powered products such as vertical voice agents, AI copilots, workflow automation tools, and AI documentation systems tailored to vertical markets
- Own the product from prototype through launch, including reaching first paying customers
- Integrate AI capabilities with existing systems: CRM, ERP, internal tools, SaaS platforms, and third-party APIs
- Build agentic workflows including multi-agent coordination, decision engines, and tool-using AI agents
Ship and Iterate
- Move fast from idea to MVP to production — bias toward working software over documentation
- Run beta tests with real users, gather feedback, and improve continuously
- Optimize AI agent performance: latency, cost, accuracy, and human intervention rate
- Ensure production reliability through monitoring, evaluation, and model lifecycle management
- Collaborate cross-functionally with product, engineering, and go-to-market teams
What This Role Is Not
- Not a research or proof-of-concept role — everything you build ships to real users
- Not a traditional backend or systems engineering role focused on ticket execution
- Not bound by story points, sprint rituals, or rigid SDLC stages
- Not limited to internal tooling — what you build may become a product customers pay for
Requirements
- Proven track record of building products, tools, automations, or side projects that went live and created real impact
- Strong working knowledge of AI and machine learning tools, large language models (LLMs), and agent frameworks
- Ability to go from zero to functional prototype quickly
- Comfort with ambiguity — you will often be defining the problem as much as the solution
- Bias toward action over planning
Technical Skills
- Hands-on experience with LLM integrations, RAG architecture, and prompt engineering
- Proficiency with workflow orchestration and automation platforms (e.g. n8n, Zapier, Make, LangChain, CrewAI)
- Ability to build and maintain API integrations across CRM, ERP, and SaaS systems
- Understanding of vector databases, embedding pipelines, and model hosting
- Strong debugging and problem-solving skills across full AI system stacks
Mindset & Approach
- Outcome-oriented: you measure success in labor hours saved, revenue generated, and adoption rate — not tickets closed
- Strong operational intuition — ability to understand a business workflow and identify where AI creates the most leverage
- Comfortable working directly with business operators, not just engineering teams
- Ability to reason about AI system limitations, failure modes, and appropriate human-in-the-loop design
Tech Stack