AI Workflow

We are a Singapore-based venture studio that invests, builds, and operates across multiple real-world sectors, including asset allocation and capital oversight, blockchain and compute infrastructure, racing operations, and cross-region construction and supply chain projects. This role is not about building an AI product to pitch or sell. It is about embedding AI directly into high-stakes operational workflows so our teams work with AI as an active teammate — today, not in the future. You will help standardize and automate core processes , turning messy, real-world problems into reusable workflows, internal tools, and AI agents that are actually adopted and relied upon. You will have direct access to decision-makers, frontline context, and operational environments across the US, China, Japan, Southeast Asia, and Europe. This is a hands-on, experimental role focused on shipping practical systems that carry real responsibility in live environments. The position is based in Singapore. Key Responsibilities Workflow Modeling & Prototyping 1. You will decompose a business line into "input → processing → decision output," and formalize it into a workflow that can be handed off and reused. Typical domains include: Investing & asset management: post-investment tracking, quant/arb exposure and capital flow, risk surfacing, monthly/quarterly reporting Energy / mining / data center ops: output, power draw, anomaly alerts, operational efficiency Racing & sim racing ops: race prep checklists, parts/gear status, logistics and critical notes, post-race review Construction / build / supply chain: milestone tracking, vendor status, time-critical approvals and blockers. 2. Your goal is not just documentation. You're productizing that workflow into a "virtual teammate": Scheduled operational briefs Live checklists that keep themselves updated Automated risk/exception alerts Consolidated status summaries that point to "who needs to do what next"3. You'll assemble MVPs using modern AI infrastructure, for example: Calling LLM APIs (including tool/function calling and action execution)Building domain-specific conversational assistants with contextual memory Adding retrieval-augmented generation (RAG) and vector search so the assistant actually understands our contracts, ops logs, site notes Orchestrating agent / tool chains so the model doesn't just "chat," it executes, logs, and pushes updates Delivering this as something teammates can literally work with day to day (an internal assistant, a briefing bot, a risk broadcast channel)The success metric is simple: does the team actually start using it. Knowledge Capture & Structuring Knowledge Capture & Structuring involves dealing with voice notes, chat logs, emails, on-site verbal updates, ops dashboards, handwritten notes — chaos. Your job is to turn that into: Queryable Maintainable Handover-ready Machine-readable structured knowledge This means enforcing consistent fields, timestamps, ownership, and state definitions so that systems can reference it, report on it, and hand it off — without relying on "who remembers."Automation & Efficiency You'll identify where humans are still patching gaps manually — and then remove that manual layer. You'll define a concrete success condition for each automation, e.g. "At 09:00 daily we get a briefing with current exposure, anomalies, and named owners for next actions."You'll deliver something that hits that standard, then iterate it forward from "prototype" into "infrastructure."Collaboration & Resource Pull You're not an anonymous execution resource sitting inside a silo. You are expected to: Tell us what's actually highest priority right now Tell us what data, access, or people you need Take a messy pain point from A to a reusable solution at B, with minimal supervision Education Bachelor's Degree in Computer Science / Software Engineering, or equivalent self-taught technical capability. Skills & Requirements Based in Singapore; able to collaborate in-person at high bandwidth (fresh grads, valid work pass holders, PR welcome). Open to non-fixed travel: when required, you may fly from Singapore to on-site locations in China, Japan, Southeast Asia, the US, or Europe to understand real conditions on the ground. Bilingual in Chinese and English (written and spoken), able to extract and structure critical information in both languages. Hands-on engineering ability: data fetching, API integration, automation scripting, or lightweight internal tooling. Strong structuring and documentation skills: you can turn scattered, informal inputs into standardized assets instead of tribal knowledge. Strong self-management; comfortable in a goal-driven, not task-driven, environment. Nice-to-haves You've built or maintained internal tools, automation scripts, dashboards, alert/notification bots, ops or risk briefs, or milestone trackers. You've played a product role, or personally drove a small internal system from idea to "people actually use this."You've standardized a process: you turned something informal and person-dependent into a template with scheduled outputs and clear ownership. Familiarity with any of: LLM APIs, vector retrieval / RAG, agent / tool-calling workflows, automation schedulers, internal chat/assistant interfaces. You're curious (or already interested) in at least one of: Investing, capital deployment, quant/arb strategy monitoring Mining, energy efficiency, data center operations Racing / sim racing preparation, execution, review Construction / build / supply chain / milestone control #J-18808-Ljbffr