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Remote AI Agent Jobs

Browse vetted remote roles building autonomous AI agents — tool-use, multi-agent orchestration, and production agentic systems that go beyond chatbots.

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Remote worldwide
All job types
AI Agent Developer LLM Application Engineer Applied AI Engineer Agentic Systems Engineer AI Product Engineer

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Jobs & Projects

Airbyte
senior
Full-time
English
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Airbyte
Full-time
English
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Snowflake
senior
Full-time
English
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Vapi
staff
Full-time
English
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Snowflake
Full-time
English
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Snowflake
senior
Full-time
English
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Pika
principal
Full-time
English
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Hex
Full-time
English
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ClickHouse

Posted 4 days ago

Growth Engineer

Full-time
English
prompt engineering ai agents node.js react a/b testing +1 more
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Pika

Posted 4 days ago

Research Scientist, Data

Full-time
English
python aws sql azure machine learning +5 more
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Role guide

What to expect in remote AI Agents jobs

AI agent engineering covers systems that plan, use tools, and take multi-step actions autonomously — a step beyond single-turn LLM calls. Remote agent roles involve designing tool-use interfaces, managing agent memory and state, and — the part most tutorials skip — building the guardrails that keep an autonomous system from doing something costly when it goes wrong.

What you'll work on

  • Design multi-agent systems where specialized agents collaborate on a task
  • Build tool-use interfaces that let an LLM safely call internal APIs and databases
  • Implement agent memory and state management for multi-step, long-running tasks
  • Add guardrails and human-in-the-loop checkpoints for high-stakes autonomous actions

What companies look for

  • Hands-on experience with agent frameworks (LangGraph, CrewAI, AutoGen, or custom orchestration)
  • Production thinking about failure modes — what happens when an agent gets stuck in a loop or calls the wrong tool
  • Evaluation practices specific to agents, not just single-turn LLM outputs

Frequently asked questions

What remote AI agent jobs are posted on RemoteAI?

Roles building autonomous or semi-autonomous LLM systems — task planning, tool-use, and multi-agent orchestration. Most require production LangChain or equivalent framework experience, not just prototype-level agent demos.

How is AI agent engineering different from regular LLM application work?

Agents make multi-step decisions and take actions with real consequences, so the engineering focus shifts toward reliability, guardrails, and failure handling — a badly-designed agent that calls the wrong tool or loops indefinitely is a production incident, not just a bad response.

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