RAG AI Developer (LLM + Retrieval) – EdTech

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

On-site – Girgaon , Mumbai

1+ year (hands-on)

Full-time