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RAG AI Developer (LLM + Retrieval) – EdTech

AP Guru

2 - 5 years

Mumbai

Posted: 12/02/2026

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Job Description

Job Summary:

We are looking for a RAG (Retrieval-Augmented Generation) AI Developer to build and improve AI features for our EdTech productssuch 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 templates, guardrails, and citation-based answers.
  • Deploy services/APIs (FastAPI/Flask), monitor latency/cost, and implement caching strategies.
  • Collaborate with product/content teams to define data sources and user workflows.


Required Skills & Qualifications:

  • 1+ year experience building NLP/LLM features (must have some hands-on RAG or retrieval work).
  • Strong Python skills.
  • Experience with embeddings, chunking strategies, and document loaders (PDF/HTML/Doc).
  • Familiarity with at least one vector DB and retrieval methods (cosine similarity, MMR, etc.).
  • Understanding of basic ML concepts and text preprocessing.


Preferred (Nice to Have):

  • Experience with OpenAI / Anthropic / Google / open-source LLMs (Llama, Mistral, etc.).
  • Experience with OCR pipelines (for scanned PDFs), speech/text, or multilingual content (helpful for EdTech).
  • Experience with Docker, cloud deployment (AWS/GCP/Azure), CI/CD.
  • Prior work on chatbots, tutoring systems, or knowledge bases.


What Success Looks Like (KPIs):

  • Higher answer accuracy + lower hallucination rate
  • Faster retrieval latency and lower compute cost
  • Clear citations and better user satisfaction on Q&A flows


Location: On-site Girgaon , Mumbai

Experience: 1+ year (hands-on)

Job Type: Full-time

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