RAG AI Developer (LLM + Retrieval) – EdTech

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.
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