Senior Software Engineer, AI Agents

🕒 April 15

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Reap

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2018

💳 Fintech

₿ Crypto

🤝 B2B

Fintech • Crypto • B2B

Reap is a fintech company providing borderless, stablecoin-enabled financial infrastructure for businesses. It offers business accounts (Reap Direct), Visa corporate cards with fiat and stablecoin repayment, cross-border payments powered by stablecoins, expense management for multi-currency treasuries, and embedded finance APIs including card issuing and payment APIs to automate global fiat payouts and enable stablecoin-native finance.

📋 Description

• Take our card operations agent from internal pilot to production — building the reliability, observability, and guardrails needed for a system handling real financial data and PII. • Own the tradeoffs between latency, model selection, cost, and safety — making pragmatic architectural decisions that keep our unit economics viable as we scale. • Build agent systems that are proactive, not reactive — designing solutions that anticipate what finance teams need rather than waiting to be asked. • Expand agent capabilities across accounting automation, policy enablement, and card operations — working with the AI Product Lead to prioritise what moves the needle for adoption. • Stay sharp on the frontier of LLM research and tooling — evaluate new models, methods, and architectures and bring what works into our stack. • Think like a product engineer, not just an AI engineer — every system you build should drive platform adoption and make clients' lives measurably easier.

🎯 Requirements

• 8+ years of experience in full-stack or backend development — with strong proficiency in Python as your primary stack. • Experience with Java, Golang, or Rust is equally welcome. • Strong software engineering foundation — experience designing distributed systems, APIs, and scalable backend architectures. • 1–2+ years of hands-on experience building AI agents in production — you've gone beyond prompting LLMs and have shipped systems that reason, plan, and act. • Deep understanding of LLM internals — you know how models work under the hood, not just how to call an API. • Architectural thinking beyond frameworks — you can evaluate when LangChain, LangGraph, AutoGen, or other agent frameworks are the right tool, and when to build from first principles. • Familiarity with Model Context Protocol (MCP) — you understand what MCPs are and how they enable agent-tool interoperability. • Leadership and delegation instincts — you're comfortable guiding and reviewing the work of others, and you operate with ownership over outcomes, not just tasks. • Excellent communication skills — you can translate complex technical decisions into clear reasoning for engineering and product stakeholders.

🏖️ Benefits

• 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