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.
• 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