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):