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Ohalo Genetics is scaling from R&D into operational maturity as its Boosted Breeding™ technology transforms agricultural biology. The company seeks a Product Manager to lead AI-Native Business Systems—a company-wide infrastructure and operations role bridging core platform capabilities with enterprise execution across Commercial Ops, G&A, and backoffice functions.
This is not a traditional RevOps or BI role. You will partner with infrastructure and platform engineering teams to deploy AI agents and agentic workflows across the entire organization. Key responsibilities include: scaling company-wide AI adoption through agent frameworks and MCP (Model Context Protocol) tooling; defining governance for agent skills and tool access; establishing evaluation frameworks and quality standards for AI agent performance (latency, accuracy, cost); architecting enterprise reporting and KPI standardization to create a single source of truth for leadership decision-making; identifying and automating repetitive manual workflows; personally prototyping v1 solutions to validate proof-of-concept before platform hardening; and enforcing governance to prevent tool sprawl and data silos.
You will own extreme operational ownership—building scrappy prototypes yourself, not just writing specs. The role demands hands-on fluency with modern AI tooling, agentic coding, and the ability to unify fragmented, manual processes into scalable automated systems. You'll influence cross-functionally across Commercial, Operations, G&A, and Engineering without direct authority, earning trust through shipping useful tools and effective collaboration.
Ideal candidates bring 5+ years of product management experience building internal platforms or operational tooling with company-wide scope. Technical foundation in SQL/Python and modern API/data architecture is essential. Deep understanding of LLM evaluation frameworks, MCPs, and agent orchestration is required. Bonus experience includes AI Ops/MLOps partnerships, BizOps/ProductOps functions at high-growth startups, graph databases, or data-rich verticals like robotics, life sciences, or advanced manufacturing.
