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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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Posted 2 months ago
Forward Deployed Engineer, Agentic Platform (Europe)
Who are we?Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are de...
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Posted 2 months ago
Staff Product Manager, New Verticals
Why HarveyAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, a...
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Posted 2 months ago
Senior Data Intelligence Engineer
Company OverviewDeepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-te...
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Posted 2 months ago
Senior Software Engineer, Agents
Why HarveyAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, a...
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Posted 2 months ago
Research Engineer, Knowledge Team
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users...
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UnWired Web Solutions is a Canadian digital marketing, web and SEO agency that builds AI-driven platforms. We produce local landing pages at scale for...
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UnWired Web Solutions is a Canadian digital marketing, web and SEO agency that builds AI-driven platforms for clients and our own products. We need a...
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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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