Senior Software Engineer, AI Agents

Reap

Develop AI agents for fintech systems, ensuring secure management of financial data and PII. Involves leading development from pilot to production, enhancing agent capabilities.

Posted 4/15/2026full-timeRemote • 🇸🇬 SingaporeSeniorWebsite

Tech Stack

Tools & technologies

Distributed SystemsGoJavaPythonRust

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonJavaGolangRustdistributed systemsAPIsscalable backend architecturesAI agentsLLM internalsarchitectural thinking

Soft Skills

leadershipdelegationcommunicationownershipproblem-solvingproactive thinkingcollaborationadaptabilitycritical thinkingstakeholder engagement