AI Coding & CLI Engineer

Syntic AI, Inc.

United States

Remote

USD 120,000 - 180,000

Full time

14 days+

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Job summary

Syntic AI, Inc. is seeking a backend-focused engineer to advance the runtime and backend capabilities that power AI agents, including orchestration, tool use, memory, and recovery. The role centers on building robust agent workflows and safe operation.

You'll design for observability, failure recovery, and measurable evaluation of task success, with emphasis on TypeScript or Python and production-grade backend systems.

Qualifications

  • Production experience with LLM-powered applications or agents.
  • Strong backend engineering skills in TypeScript or Python.
  • Understanding of failure modes associated with tool-using models.

Responsibilities

  • Build orchestration systems for agent tasks and workflows.
  • Implement tool-calling capabilities for agents.
  • Develop measurable evaluations to assess and improve task success.
  • Make agent behavior observable and support safe interruption.
  • Design for recovery from failures in tool-using model workflows.

Skills

LLM-powered apps
TypeScript
Python
Agent orchestration
Failure modes

Job description

Role overview

Work on the runtime and backend capabilities that support AI agents, including planning, tool use, memory, and recovery. The description does not detail the specific coding or CLI product scope associated with this title; the stated work is focused on agent orchestration, evaluation, and safe operation.


Responsibilities


  • Build orchestration systems for agent tasks and workflows.

  • Implement tool-calling capabilities for agents.

  • Develop measurable evaluations to assess and improve task success.

  • Make agent behavior observable and support safe interruption.

  • Design for recovery from failures in tool-using model workflows.


Requirements


  • Production experience with LLM-powered applications or agents.

  • Strong backend engineering skills in TypeScript or Python.

  • Understanding of failure modes associated with tool-using models.

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