Founding Head of Engineering

Nth AISan Francisco, CA

About The Position

Build the next generation of enterprise AI software. Nth AI is building Nexus, an AI-native platform that creates the enterprise context layer for AI. Built inside Microsoft Azure and Fabric, Nexus automates the data integration, semantic modeling, and governance work required to bring enterprise AI into production. We’re hiring a Founding Head of Engineering to own the engineering execution behind that vision. You’ll work directly with the founders to shape the architecture, build the team, and turn an emerging product into reliable enterprise software. This is a hands-on leadership role. You’ll make architecture decisions, review and write code, resolve difficult technical problems, and establish how the engineering organization delivers.

Requirements

  • Experience leading engineering teams while remaining technically hands-on.
  • A track record of shipping production B2B software and taking early products through enterprise adoption.
  • Strong backend and distributed-systems judgment, including APIs, asynchronous jobs, state management, integrations, and failure recovery.
  • Meaningful experience with data platforms or production AI systems, with the ability to lead across both.
  • Experience hiring strong engineers, clarifying ownership, and improving delivery without introducing unnecessary process.
  • The ability to make sound tradeoffs under ambiguity and communicate them clearly to technical and business stakeholders.

Nice To Haves

  • Microsoft Azure, Fabric, Azure AI Foundry, agent orchestration, semantic systems, and software deployed inside customer-controlled environments.

Responsibilities

  • Core product engineering. Translate product priorities into clear technical plans and ship complete capabilities across Nexus’s backend, orchestration, data workflows, and user experience.
  • Architecture and reliability. Establish clear service boundaries, durable workflow execution, useful observability, and maintainable interfaces across the platform.
  • AI engineering discipline. Build practices for evaluating agent behavior, validating generated assets, handling failures, and preserving human review where it matters.
  • Team building. Recruit and develop exceptional software, data, and AI engineers. Set clear ownership and a high bar for technical judgment.
  • Engineering execution. Establish practical planning, code review, testing, release, and incident practices that support fast, dependable shipping.
  • Enterprise feedback into product. Partner with Product and the FDE team to turn deployment learning into reusable capabilities and prioritize the engineering work that unblocks customers.
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