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Decagon

Customer Engineer, Agent Builder - Spanish Speaking

San Francisco$175k–$230kfulltimemidAdded today

About this role

Decagon seeks a Customer Engineer to own end-to-end delivery of AI agent builds for enterprise customers. You'll architect and implement production-grade conversational AI agents, working across technical integration, customer requirements, and platform feedback loops while bringing bilingual English-Spanish capabilities.

What you'll do

  • Own full lifecycle of AI agent implementations from design through production deployment for strategic customers
  • Design and engineer agentic logic, guardrails, and supervisory controls ensuring safe and reliable agent behavior
  • Architect and validate integrations with customer systems including APIs, data pipelines, and CRM/ticketing platforms
  • Interface with senior technical stakeholders to define success criteria, requirements, and delivery timelines
  • Debug and resolve technical and probabilistic failures through trace analysis and root-cause investigation
  • Build evaluation frameworks and regression tests to validate agent performance and prevent degradation

What they're looking for

  • AI/LLM agent architecture and configuration
  • API design and integration engineering
  • Production system debugging and troubleshooting
  • Technical customer communication and requirements gathering
  • Python or similar programming languages
  • Evaluation framework and testing methodology design
  • Bilingual English-Spanish fluency (written and spoken)
  • Solutions engineering or forward-deployed engineering experience
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Decagon

Decagon builds enterprise-grade conversational AI platforms that enable organizations to deploy AI agents for business impact. The company is hiring Strategic Solutions Engineers, Customer Engineers, Platform Engineers, and systems-focused engineers to deliver AI implementations, build internal infrastructure, and establish security practices across their growing platform.

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Likely interview questions

  • Walk us through a complex integration you've built end-to-end—what were the key technical challenges and how did you solve them?
  • How would you approach designing guardrails and safety controls for an AI agent handling sensitive customer data or financial transactions?
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