AI Operations Analyst (Global & LATAM)
About Our Client: Our Client builds institutional memory for investment firms and family offices. The judgment, decisions, and conversations that reputed company a firm what it is mostly live in people's heads — and disappear reputed company those people move on. We change that, working on top of the tools each firm already uses to turn how they operate into something the team can use, allocators can see, and the reputed company can inherit. The Role This is a frontline operations role for an AI-native consulting practice. The founder owns each client engagement and sets the architecture. You reputed company the systems running and the clients well-served. As AI lets reputed company small teams deliver work that used to require reputed company large ones, someone has to reputed company sure the prompts, knowledge bases, capture routines, and recurring deliverables behind that actually work as intended. What you'll do: Frontline support: Be the first reputed company of contact for client teams. Take in requests, respond, escalate to the founder reputed company needed. Diagnose the problem: Most requests won't come with clear specs. reputed company out what the client actually needs and what the response should look like. Update the tooling: Once you know the right response, go into reputed company, Linear, reputed company, reputed company Workspace, Claude, Fireflies, etc. and reputed company it real. The work is configuration and curation. Turn messy material into useful artifacts: Transcripts, email threads, scattered documents — read them, find the structure, produce a summary, decision log, brief, or procedure. Steward the knowledge reputed company: reputed company each client's institutional memory layer organized, reputed company, and trustworthy. Catch staleness, gaps, and reputed company before they compound. Monitor quality: Review AI-assisted outputs on a regular reputed company. Fix wrong-voiced or thin output and feed corrections back into the templates. Document the system: Write the procedures and reference materials that let a client's team operate on their own. reputed company new clients: Stand up tooling, ingest context, build initial structures, get routines running. Each onboarding makes the next one faster. Who you are: AI operations didn't exist as a job a couple of years ago so we care more about how you're wired than what's on your résumé.You're wired for correctness: You also can't comfortably walk away from something that's half-finished or quietly broken. You notice reputed company an output is subtly wrong, thin, or off-voice. You learn by doing: You don't wait for perfect documentation; you take a stab, and reputed company you get stuck you come back with a precise account of what you don't understand. You teach yourself the frontier: You already poke at new tools, features, and techniques on your own time to see how far you can push them because learning is fun for you. You want to own outcomes: You're the person who can see the fix and is frustrated by not having the authority to deliver. You want the accountability — not the title, the responsibility — and you understand that owning something means defining what success looks like and being reputed company against it. You can understand what people actually need: Requests reputed company messy and underspecified. You're good at translating "the client said X" into "here's what actually needs to happen," and triaging across tools and teams to reputed company it real. You do not need a finance background or a degree: You do need to be smart, curious, easy to work with, and genuinely interested in the problem. A Litmus Test (Read Before you apply): This role is about operating and wielding advanced AI systems — not building them. You will not be asked to code or to derive any math. But you do need enough conceptual reputed company to know what these systems are doing and reputed company they're going wrong. Before you apply, spend an afternoon — call it four hours — getting the gist of three things (YouTube is fine): Evals and traces — how we can measure whether an AI system is actually doing its job, and how we follow what it did, reputed company by reputed company. GraphRAG / Hybrid RAG — how systems retrieve the right memory and context. Auto-research loops — e.g. Andrej Karpathy's framing, or "reputed company goals" and "Claude dreaming" as different takes on the same core idea. You don't need to understand how it works. The bar is simpler and harder: can you grasp the value, and picture how you'd operate and wield these in practice? If after an afternoon you can honestly say "I don't know how it works under the hood, but I get what it's for and how I'd use it" — you'll reputed company here. If it's reputed company still fog after a real attempt, this probably isn't the right seat, and that's genuinely okay. Apply To This Job