Engineering Manager, AI

About Reap

Reap is a global financial technology company headquartered in Hong Kong with employees across multiple countries. We enable financial connectivity and access for businesses worldwide by combining traditional finance with stablecoins for efficient money movement.

About Reap

Reap is a global financial technology company headquartered in Hong Kong with employees across multiple countries. We enable financial connectivity and access for businesses worldwide by combining traditional finance with stablecoins for efficient money movement.

About Reap

Reap is a global financial technology company headquartered in Hong Kong with employees across multiple countries. We enable financial connectivity and access for businesses worldwide by combining traditional finance with stablecoins for efficient money movement.

Through our stablecoin-powered corporate cards, payments, and expense management tools, we streamline financial operations and help businesses scale. Our APIs enable businesses to integrate stablecoin-enabled finance into their own products and services—from issuing Visa cards to facilitating cross-border payments.

Backed by leading investors including Acorn Pacific, Index Ventures and HashKey Capital, Reap is building the future of borderless, stablecoin-enabled finance.

Why Reap

Reap runs a regulated, multi-jurisdiction card and payments business — which means a large share of our work is operational: onboarding and KYB reviews, compliance screening, transaction and fraud investigations, reconciliation, disputes, and customer support. Every one of those workflows is a documented SOP, a set of systems, and a queue of human judgement calls.

We are rebuilding that layer to be AI-native. We have already exposed our operational and financial data through MCP servers that Lorikeet queries to answer customer questions and that operations teams query to interrogate live data. We have embedded AI into end-to-end workflows so that agents execute most of an SOP and surface findings for human approval. Teams across the company are shipping their own AI-built internal dashboards, and our engineers are building AI skills and workflows into their daily development loop.

This role exists to turn that momentum into an engineering discipline — with an owner, an architecture, a governance model, and a team.

What You'll Do

As Engineering Manager, AI , you will lead the engineering effort to make AI a production capability across Reap, not a collection of experiments. You will own the AI platform layer — MCP servers, agentic workflows, internal AI infrastructure, and the enablement that gets other teams building safely on top of it.

This is a high-ownership, hands-on leadership role . You will set technical direction, stay in the code and the design reviews, and work across Engineering, Operations, Compliance, Finance, and Support. You are measured on adopted, production systems and the operational leverage they create — not on prototypes.

• Agentic Data Layer
• Own the design, security model, and roadmap of Reap's agentic data layer — the interface through which Lorikeet, internal agents, and operations teams query live business data
• Define how MCP servers and agent-facing tools are scoped, authenticated, permissioned, rate-limited, and audited, so that access to production financial data is safe by construction and provable after the fact
• Expand coverage to new domains (cards, payments, treasury, onboarding, compliance, support) and set the standards that keep tool definitions consistent, discoverable, and reliable under agent use
• Build the evaluation and observability layer: measure answer quality, tool-call correctness, latency, and failure modes, and act on what you find
• Agentic Operational Workflows
• Lead the redesign of end-to-end operational workflows so agentic systems execute the bulk of the SOP and present findings for human approval, with explicit checkpoints where a human decision is required
• Work directly with Compliance, Operations, Finance, and Support to translate written SOPs and real pain points into scoped, buildable agentic workflows
• Make “agentic first” the default way Reap approaches new opportunities and workflows, by building the tooling, patterns, and enablement that let teams move quickly and safely
• Design for auditability first: error handling, retries, rollback, structured audit trails, and clear escalation paths on every workflow that touches regulated data or customer money
• Prioritise the pipeline by operational leverage and risk — and be willing to say no to automation that cannot be made safe
• AI Infrastructure and Governance
• Own the internal AI platform that other teams build on: shared model access, secrets an