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 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). - Solid 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: In office – Girgaon , Mumbai

Experience: 1+ year (hands-on)

Job Type: Full time

📌 Rag Ai Developer Llm + Retrieval – Edtech Mumbai (India)
🏢 AP Guru
📍 India