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Browse remote LangChain positions — LLM app development, RAG systems, AI agents, and production LLM orchestration at companies building real AI products.
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We're building a developer platform for LLM applications — think eval frameworks, observability dashboards, prompt versioning, and deployment tooling....
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Posted 7 months ago
Applications closedNebula AI
Remote
We're a 6-person AI startup building a product for the legal industry. We have strong early traction and just closed a pre-seed round. We need our sec...
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Posted 7 months ago
Applications closedTerraVision AI
Remote
We're releasing a consumer-facing AI assistant in 8 weeks. Before we do, we want it red teamed properly. We need an AI red team specialist to systemat...
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Posted 8 months ago
Applications closedMediaMind AI
Remote
E-commerce platform, 2M users across France, Germany, Spain and Italy. Our support volume is 60% repetitive questions — shipping times, return policie...
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Posted 8 months ago
Applications closedAxiom Labs
Remote
We run a SaaS platform for e-commerce merchants. We want to build a fine-tuned support bot that handles 80% of incoming tickets automatically — refund...
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Role guide
LangChain has become the primary framework for building production LLM applications. Remote LangChain jobs involve designing RAG pipelines, building autonomous AI agents, and integrating LLMs into real products. The strongest candidates have experience going beyond documentation examples to debug real production LLM behaviour.
What remote LangChain jobs are posted on RemoteAI?
LLM application development roles, RAG system engineering positions, and AI agent development jobs. Most require Python proficiency and hands-on experience building LLM-powered features beyond simple API calls.
Do LangChain jobs require knowledge of vector databases?
Most RAG-focused LangChain roles do. Pinecone, Weaviate, Chroma, and pgvector are common. The exact stack varies by company — check individual listings.
Is LangChain experience enough, or do I also need raw LLM API experience?
Both help. Companies value engineers who understand what LangChain does under the hood — prompt formatting, chain execution, memory management — not just the high-level API.
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