Principal AI-Native Engineer, @Ionic Partners

Most companies are running AI pilots. We are rebuilding how our companies actually operate. Ionic Partners is a holding company with a portfolio of operating businesses. We are not adding AI features to products or rolling assistants out to employees. We are rebuilding the operating systems of entire business functions — engineering, support, finance, marketing, revenue operations — as agentic systems that do the work, in production, every day. We are looking for Principal AI-Native Engineers to build them. This is not a plan we are pitching. Agents already write and review code inside our engineering pipeline. Our own orchestration framework is what those systems run on, and it is on its second generation because the first one taught us what breaks. The knowledge our agents work from is maintained as structured, machine-readable artifacts rather than scraped from wikis, because we learned early that context quality sets the ceiling on everything else. Every hard-won lesson in that sentence cost us something. You will inherit all of it, and the next set of lessons is yours to learn. This is a central team, deliberately flat, that builds agentic systems for every company in the portfolio. There are no internal layers, no leads, and no junior tier. Everyone on it is a principal-level builder reporting directly to the executive who owns the mandate. Scope is concentrated rather than distributed: each engineer carries an unusually large share of the outcome and an unusually direct line to the decisions that matter. You will not be starting from scratch. We already run a production agentic platform in-house — our own orchestration framework, machine-readable context artifacts that give agents grounded knowledge of our products and customers, and automated development and QA agents working inside our engineering pipeline. Your first job is to ramp into a live system that real businesses depend on. Your ongoing job is to extend it, harden it, and use it to make each new function agentic faster than the last. The team is domain-agnostic by design. Subject-matter experts inside each business define what their function needs. You design and build the systems that deliver it. You do not need to be an expert in finance, support, or revenue operations. You need to be exceptional at turning a well-specified problem into a reliable agentic system that runs without you standing over it. And you own what you build. Not a prototype handed to another team. Not a proof of concept that dies in a demo. You ship it, you run it, you watch its evals, you carry its cost line, you improve it. If it breaks, it is yours. If it transforms how a company works, that is yours too. This is not an applied research role, and it is not an AI enablement or transformation role. Your work product is not a strategy, a recommendation, or a rollout plan — it is a working system in production, operating a real business function, measured on whether that function got materially better. The distance between an impressive agentic prototype and a system a business will actually depend on is where this entire job lives: the eval harnesses, the failure modes nobody anticipated, the guardrails, the traces, the fallback paths, the cost per run at volume, the second year of maintenance. It is also not a management role, and it does not become one. There is no reporting line beneath you and no expectation that you build one. Influence here comes from what you ship and the standard you set for the engineers and operators around you. We built the team flat specifically so we would never have to ask an elite builder to stop building to advance. The bar is high, and we intend to keep it there. Each hire is a large fraction of this team, and we would rather stay understaffed than lower it. If you want to build agentic systems that run real businesses — and stay technical while doing it — we would like to hear from you. **Responsibilities** * Partner with subject-matter experts across the portfolio companies to turn functional specs into agentic system designs, pressure-testing scope and surfacing failure modes before build. * Design agent architectures: agent boundaries, orchestration and control flow, tool surfaces, state and memory, human-in-the-loop placement, and the split between model-driven and deterministic logic. * Build, ship, and operate production agentic systems that run real business functions end to end. * Extend and harden the in-house orchestration framework — execution control, integrations, permissioning, observability, failure, and recovery behavior — so each build raises the floor for the next. * Improve the automated development and QA agents already running inside our engineering pipeline. * Create and maintain machine-readable context artifacts covering products, customers, processes, and operational data, and keep them current as the businesses change. * Design retrieval and context-assembly strategies, manage context