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