Job Description
Job Summary:
We are looking for a
RAG (Retrieval-Augmented Generation) AI Developer
to build and improve AI features for our EdTech products—such as course Q&A bots, tutor assistants, content search, and internal knowledge assistants. You will work on document ingestion, embeddings, retrieval pipelines, evaluation, and deployment.
Key Responsibilities:
Build and maintain
RAG pipelines
: ingestion → chunking → embedding → vector storage → retrieval → generation.
Implement hybrid search (semantic + keyword), reranking, filters, and metadata-based retrieval.
Integrate LLMs with tools/frameworks (e.g.,
LangChain / LlamaIndex
or custom pipelines).
Work with
vector databases
(e.g., Pinecone, Weaviate, FAISS, Chroma, Milvus) and optimize retrieval performance.
Create evaluation metrics for RAG quality (faithfulness, relevance, context precision/recall) and reduce hallucinations.
Build prompt ...
We are looking for a
RAG (Retrieval-Augmented Generation) AI Developer
to build and improve AI features for our EdTech products—such as course Q&A bots, tutor assistants, content search, and internal knowledge assistants. You will work on document ingestion, embeddings, retrieval pipelines, evaluation, and deployment.
Key Responsibilities:
Build and maintain
RAG pipelines
: ingestion → chunking → embedding → vector storage → retrieval → generation.
Implement hybrid search (semantic + keyword), reranking, filters, and metadata-based retrieval.
Integrate LLMs with tools/frameworks (e.g.,
LangChain / LlamaIndex
or custom pipelines).
Work with
vector databases
(e.g., Pinecone, Weaviate, FAISS, Chroma, Milvus) and optimize retrieval performance.
Create evaluation metrics for RAG quality (faithfulness, relevance, context precision/recall) and reduce hallucinations.
Build prompt ...
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