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 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

📌 RAG AI Developer (LLM + Retrieval) - EdTech (Mumbai)
🏢 AP Guru
📍 Mumbai