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Senior Software Engineer, AI Agents

NEW
Hong Kong, Singapore, Malaysia, ...Full-timeGlobal
šŸ“Š MidšŸ  Remote
ActivePosted within the last 30 days

Job Description

[AI-summarized by JobStash]

You will take an internal card operations agent from prototype to production, improving reliability, observability, and safety for systems that handle financial data and PII. You will make pragmatic tradeoffs between latency, cost, and model selection, expand agent capabilities into accounting automation and policy enablement, evaluate new LLMs and architectures, and design agent systems that proactively assist finance teams. You will work closely with the AI product lead, own architectural decisions, and build production-grade, compliant agent tooling.

Requirements

  • ā—8+ years of full-stack or backend development experience
  • ā—Proficiency in Python (primary) with experience in Java Go or Rust welcomed
  • ā—Strong foundation in designing distributed systems APIs and scalable backend architectures
  • ā—1ā€“2+ years building AI agents in production beyond prompting
  • ā—Deep understanding of LLM internals
  • ā—Ability to evaluate when to use agent frameworks versus building from first principles
  • ā—Familiarity with Model Context Protocol (MCP)
  • ā—Leadership instincts and experience guiding and reviewing others
  • ā—Excellent communication skills
  • ā—Nice to have: fintech payments or financial services experience
  • ā—Nice to have: Node.js and TypeScript
  • ā—Nice to have: MLOps and AI deployment pipeline experience
  • ā—Nice to have: multi-agent coordination tool use memory architecture and agentic evaluation observability tooling

Responsibilities

  • ā—Take card operations agent from internal pilot to production
  • ā—Build reliability and observability for systems handling financial data and PII
  • ā—Design and implement guardrails for safety and compliance
  • ā—Own tradeoffs between latency model selection cost and safety
  • ā—Expand agent capabilities across accounting automation policy enablement and card operations
  • ā—Evaluate new LLMs methods and architectures and integrate effective approaches
  • ā—Design proactive agent systems that anticipate finance team needs
  • ā—Make product-focused engineering decisions to drive adoption and unit economics

Benefits & Perks

  • ā—Insurance coverage after probation
  • ā—Reap Card stipend
  • ā—Use of AI tools at work and space to experiment and learn

Tech Stack

RustJavaAPIMCPTypeScriptNode.jsGoLLMMemory architectureModel Context Protocolproject:Reap
Expired
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