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Decagon

Builds AI agents for enterprise customer support

Agent Deployment Engineer

AI Application EngineeringUpdated on Nov 10
Full-timeAustralia

About the Job

Requirements

  • Have at least 5 years of relevant experience in a technical customer-facing role, such as solutions engineering, forward-deployed engineering, technical consulting, implementation engineering, technical product or product management, or a similar role.
  • Have a strong technical foundation, including the ability to write code, work with application programming interfaces, and build and validate integrations end-to-end.
  • Have experience delivering production-grade customer solutions requiring structured execution, testing, validation, and iteration.
  • Be able to communicate with senior technical stakeholders, translate requirements into implementation plans, and drive delivery.
  • Be comfortable working in fast-moving, ambiguous environments and shaping solutions while implementing them.

Responsibilities

  • Own end-to-end architecture and execution of AI agent builds for enterprise customers, from systems design and initial scoping through implementation, evaluation, and production deployment.
  • Design and engineer the core agentic logic governing agent behavior, applying engineering principles to optimize quality, reliability, and correctness across non-deterministic model outputs.
  • Engineer and validate layered guardrails and supervisory controls to ensure safe, compliant, and predictable agent performance across real-world scenarios.
  • Architect, build, and test integrations with customer systems, including data pipelines, customer relationship management systems, and ticketing systems, and build the tools, application programming interfaces, and workflows needed for reliable deployments at scale.
  • Interface with senior technical stakeholders at customers to define success criteria and system requirements, and drive technical delivery against timelines.
  • Diagnose, debug, and resolve probabilistic and technical failures through root-cause analysis of execution traces, error logs, and model behavior.
  • Design evaluation and regression-testing frameworks to validate agent behavior against ground truth and guard against performance drift.
  • Translate customer needs into clear internal documentation and run feedback loops with Engineering to drive platform improvements.
  • Partner with Agent PMs, Engineering, Design, and Go-To-Market teams to deliver consistent, repeatable agent builds.

Desired Qualifications

  • Experience building with or around large language models or AI agents, including prompting, evaluation, guardrails, tooling, or workflow design.
  • Experience with enterprise software-as-a-service integrations, such as ticketing systems, customer relationship management systems, or data pipelines, and associated security and compliance considerations.
  • A degree in Computer Science, Engineering, or Math, or equivalent technical experience.
  • Strong product instinct, including the ability to write concise product requirement documents, define success metrics, and contribute customer insight to the product roadmap.

About the Company

Decagon

Builds AI agents for enterprise customer support

Decagon builds AI customer support agents for large businesses, handling customer questions and requests over voice, chat, email, and text messages. Its platform connects these conversations to company records and internal tools, so an agent can check an order, change a reservation, or complete a support task. Customer experience teams define how agents should respond and when to pass a case to a person, while testing tools check behavior before changes reach customers. Businesses pay usage fees for the software, with charges tied to conversations or completed resolutions rather than the number of human support staff. Decagon was founded in 2023 and is headquartered in San Francisco.

Company size
501-1,000
Company stage
Series D
Total funding
$481M
Headquarters
San Francisco, CA
Founded
2023

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