Senior Software Engineer, AI Agents

• 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.

• 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.
  

Nice to Have

• Experience in fintech, payments, or financial services (though strong engineers adapt — domain knowledge can be learned).

• Proficiency in Node.js and TypeScript.

• Familiarity with MLOps practices and AI deployment pipelines.

• Experience with multi-agent coordination, tool use, and memory architectures.

• Prior exposure to agentic evaluation and observability tooling.

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

• Flexible hybrid 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