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.

1. 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

2. 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

3. AI Infrastructure and Governance

• Own the internal AI platform that other teams build on: shared model access, secrets and data handling, deployment paths, and a secure hosting environment (Vercel and equivalent) with sensible defaults

• Support teams shipping their own AI-built internal dashboards and tools — provide the paved road, run pragmatic security reviews, and get them to launch quickly without loosening the bar

• Design governance so the compliant path is the fastest path: keep friction low, preserve engineering velocity, and reduce the incentive for teams to work around the process

• Classify builds by risk tier before work begins. Nothing reaches production without a named owner, documented data flows, confirmed access controls, and logging in place

• Set the standards for handling customer PII and regulated data in AI systems, in partnership with Security, Compliance, and Legal across the jurisdictions we operate in

4. Engineering Enablement and Team Leadership

• Raise the AI fluency of Reap's engineering organisation: build the skills, workflows, tooling, and internal documentation that improve developer experience and delivery speed

• Run enablement as a product — measure adoption and impact, iterate on what engineers actually use, and retire what they don't

• Build the tooling and paved-road workflows that empower teams to apply agentic systems to new opportunities without creating avoidable compliance or security risk

• Hire, grow, and lead a small, senior team; set direction, give direct feedback, and create the conditions for high-judgement engineers to do their best work

• Report on impact in operational terms: review time reduced, throughput increased, cycle time shortened, and capacity returned to higher-judgement work

What We're Looking For

• 8+ years of software engineering experience with 2+ years in a technical leadership or management role

• Demonstrated experience designing, deploying, and driving adoption of AI or agentic systems in production, used by other people — not experimentation or internal demos

• Hands-on proficiency with the modern AI stack: Claude and other LLM APIs, AWS Bedrock, MCP or equivalent agent-tooling layers, Python or TypeScript orchestration, and workflow engines such as n8n

• Deep understanding of LLM failure modes — hallucination, tool misuse, prompt injection, silent degradation — and how to design evaluation, guardrails, and human-in-the-loop controls around them

• Strong systems thinking; able to move from discovery to design to build to deployment to optimisation without leaning on external consultants

• Ability to work directly with non-technical operators, understand their workflow end to end, and translate it into something buildable

• Sound judgement on data classification, access control, and audit requirements in a regulated financial environment

• Hold a high bar for code quality, reliability, and peer review, and value working closely with others who do the same

• Communicate clearly, challenge ideas respectfully, and help raise the quality of the whole team

• Our main stack is TypeScript, Node.js, NestJS, and AWS, but we value strong engineering fundamentals over any specific toolset

Nice To Have

• Experience building or operating at least 1–2 of the following at production scale: MCP servers, agent tooling layers, or LLM gateways

• Background in payments, cards, fintech, or another regulated financial environment, and familiarity with AML/KYC, fraud, or reconciliation workflows

• Experience deploying automation in a SOX-regulated or equivalent compliance environment, with a clear view of which controls must be preserved

• Track record of internal platform or developer-experience work — building things other engineers choose to use

• Experience running security reviews for internally built tools, or standing up a paved-road deployment path

• Familiarity with LLM evaluation frameworks, tracing, and observability tooling (LangSmith, Braintrust, OpenTelemetry, or equivalent)

• Experience leading teams in high-growth, fast-paced environments

• Prior experience standing up an AI enablement, automation, or internal platform function from scratch

Why You'll Love it Here

• A high-impact role in a rapidly growing fintech startup

• Flexible hybrid/remote work environment with a global, collaborative team

• Insurance coverage after probation

• Reap Card stipend

• Use of AI tools at work, and the space to learn, experiment, and grow with them

• A culture of innovation, inclusion, and continuous learning

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