Product Manager - AI-Native Business Systems

Ohalo•South San Francisco, CA

23h•$140,000 - $190,000•Onsite

About The Position

Ohalo is entering a phase of massive operational scale, moving beyond R&D. As our "Boosted Breeding™" technology advances, our enterprise operations, including Commercial Ops, Agentic workflows, and backoffice G&A, must scale with the same ambition. This role is for a Product Manager to bridge our core Infrastructure & AI Native Business Systems efforts with company-wide operational execution. This is a company-wide mandate, not a siloed RevOps or traditional Business Intelligence role. You will partner across infrastructure and platform teams to expand Ohalo’s core operational capabilities with AI, including harnessing agent frameworks and MCP tooling, and maintaining skills. You will ensure best practices are accessible across every department, transforming foundational AI capabilities into pragmatic, high-impact operational systems. The goal is to ensure the company runs on a trusted, unified digital layer, avoiding fragmented applications. This is a hands-on role for an operator who understands systems and can prototype solutions directly to prove value before hardening them into new platform capabilities.

Requirements

Bachelor’s degree in Computer Science, Data Analytics, or a related quantitative field preferred.
Highly fluent in SQL/Python and comfortable interfacing with modern API/data architecture.
5+ years of product management experience building internal platforms, business systems, or operational tooling with company-wide scope.
Deep understanding of modern AI technology concepts including LLM evaluation frameworks (evals), model context protocols (MCPs), and agent orchestration/harnessing.
Actively uses AI tooling, agentic coding harnesses, or vibe coding platforms in daily prototyping and workflow design. You don't just write specs; you build scrappy prototypes.
Demonstrated history of taking manual, Excel-based, or multi-department fragmented processes and turning them into scalable automated systems.
Exceptional communicator who earns cross-functional trust with Commercial, Operations, G&A, and Engineering through effective collaboration and shipping useful tools.

Nice To Haves

Direct experience partnering with AI Ops, MLOps, or Core Infrastructure teams to productize AI tools internally.
Experience in a "Business Systems," "BizOps," or "ProductOps" function at a high-growth startup.
Experience with graph databases or complex data warehouse integrations.
Background in a data-rich, operationally intensive vertical (robotics, life sciences, or advanced manufacturing).

Responsibilities

Scale Company-Wide AI Deployment: Partner across engineering teams to extend core AI capabilities across the entire business. Drive company-wide adoption of agent infrastructure.
Manage MCP & Tooling Access: Define and govern Model Context Protocol (MCP) integrations and agent skills. Ensure native agents (spanning leadership team to commercial ops and backoffice G&A) have secure, reliable access to internal tools and systems of record.
Improve Evals & Quality Standards: Stand up evaluation frameworks for operations and establish consistent feedback loops. Monitor AI agent performance, latency, accuracy, and cost-to-serve, continuously refining skills and prompts to ensure high-fidelity outputs.
Architect Enterprise Reporting & Ground Truth: Serve as the company’s absolute metric authority. Define, standardize, and govern KPIs across Commercial, Ops, People, and G&A so leadership makes decisions based on a single source of truth calculated at the system level.
Automate Operational Friction: Identify repetitive manual reporting and execution workflows company-wide. Deploy AI-native automations and measure real ROI, operational efficiency gains, and time saved.
Own Technical Delivery & Prototyping: Maintain an extreme ownership mindset. Personally prototype v1 workflows and reporting to validate proof-of-concepts before scaling across platform engineering.
Prevent Tool & System Sprawl: Enforce strong platform governance so new agent tools and dashboards plug seamlessly into Ohalo’s core architecture rather than spawning fragmented UI layers or data silos.