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Browse remote LlamaIndex positions — RAG pipeline engineering, document intelligence, and LLM retrieval system development at AI-first companies.
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CodeRabbit is the leading AI code review platform, trusted by more than 17,000 customers and 150,000 open-source projects, conducting over 2 million c...
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OpenRouter is the unified interface for large language models. We route billions of tokens every month, helping developers build AI applications witho...
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At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are en...
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Your work will have an outsized impact. Our SDKs are downloaded 26M+ times per month. When you ship a new integration or improve SDK performance, thou...
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Neo4j is the graph intelligence platform that transforms data into knowledge to power the next generation of intelligent applications and AI systems....
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Neo4j is the graph intelligence platform that transforms data into knowledge to power the next generation of intelligent applications and AI systems....
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Join us and help shape the future of AI by defining the narrative around document understanding. LlamaIndex makes enterprise data useful for AI applic...
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Posted 3 weeks ago
Staff Applied AI Engineer - Enterprise AI Solutions
Snorkel AI
New York City, NY (Hybrid); San Francisco, CA (Hybrid); United States (Remote)
Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine techn...
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Your work puts Langfuse into developers’ hands. Our SDKs are downloaded 26M+ times per month, and for many developers the first thing they touch is an...
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Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows,...
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Role guide
What to expect in remote LlamaIndex jobs
LlamaIndex is the go-to framework for building RAG systems and document intelligence applications. Remote LlamaIndex jobs involve designing retrieval pipelines, connecting LLMs to private knowledge bases, and building production systems that answer questions accurately over large document corpora. These roles require deep understanding of chunking strategies, embedding models, and retrieval evaluation.
What you'll work on
- Build RAG systems that answer questions over enterprise knowledge bases and documents
- Design multi-document retrieval pipelines with re-ranking for accuracy
- Create agents that query structured and unstructured data sources with LlamaIndex
- Develop evaluation pipelines to measure retrieval and generation quality
What companies look for
- LlamaIndex core abstractions — nodes, indices, query engines, retrievers
- Chunking strategy design and embedding model selection for retrieval quality
- Integration with vector stores (Pinecone, Weaviate, pgvector) and LLM APIs
Frequently asked questions
What is the difference between LlamaIndex and LangChain for remote jobs?
LlamaIndex specialises in document retrieval and RAG pipelines. LangChain is broader — covering agents, chains, and general LLM orchestration. Many companies use both. Job listings often list both as desirable.
Do LlamaIndex jobs require vector database experience?
Almost always. Pinecone, Weaviate, Chroma, and pgvector are the most common. Understanding how embedding similarity search works is essential for RAG engineering roles.
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