Vice President of Engineering

September 23, 2026
Application ends: December 22, 2026

Job Description

REQUIREMENTS

  • 8+ years of software engineering experience, including 3+ years leading engineering teams that shipped production software to enterprise clients
  • You have led a team that runs AI or LLM systems in production, and you know what those systems should and should not be trusted with
  • Experience running a high-volume data API that clients measure on accuracy and latency
  • Experience in financial services, payments, or another regulated industry
  • Technical depth to make architecture calls across our TypeScript, Python, and Go stack
  • Comfort with different deployment models, including API-based, private cloud, and on-premise
  • A record of growing a team yourself, including handling the hires that did not work out
  • Comfort in front of clients: you can run an architecture session with their engineers
  • Ownership mindset: you define the solution as much as you build it, and you do not wait to be unblocked
  • Understanding of payments, disputes, or chargeback workflows is a strong plus, and genuine curiosity about them is required
  • English proficiency required

RESPONSIBILITES

  • Lead engineering for the AI agents that run banking workflows and for the transaction enrichment platform behind them
  • Scale the dispute agents across a growing number of bank and fintech clients, then lead the build of the workflows that follow
  • Lead the forward deployed engineers, and make what they build for one client reusable for the next
  • Expand the team and develop the leads already in place, reshaping the structure as the platform adds products
  • Keep the enrichment API fast and available on every transaction banks and fintechs send, and keep every action an agent takes on a live dispute reliable and auditable
  • Represent engineering in client procurement and deployments, from the first architecture session to the security review
  • Set how the team builds and evaluates agents, including where a language model is allowed to decide and how accuracy is measured before a client sees the output
  • Decide what the team delegates to coding agents and how it verifies their output
  • Work alongside product and sales, and turn what the field learns into the roadmap

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