RAG AI Developer (LLM + Retrieval)

This role is for one of the Weekday's clients

Min Experience: 1 years

Location: Mumbai

JobType: full-time

We are seeking a RAG AI Developer to design, build, and optimize retrieval-augmented generation solutions for EdTech-focused AI products such as course Q&A systems, tutor assistants, content discovery tools, and internal knowledge platforms. This is a hands-on role focused on building end-to-end RAG pipelines, improving answer accuracy, reducing hallucinations, and deploying scalable AI services that deliver fast, reliable, and well-cited responses.

Requirements:
Key Responsibilities

• Build and maintain end-to-end RAG pipelines, including document ingestion, chunking, embeddings, retrieval, and generation
• Implement and optimize hybrid search strategies combining semantic and keyword-based retrieval
• Integrate LLMs using frameworks such as LangChain, LlamaIndex, or custom-built pipelines
• Work with vector databases to optimize indexing, retrieval speed, and relevance
• Design reranking strategies, metadata filtering, and retrieval tuning for improved answer quality
• Develop evaluation frameworks and metrics to measure relevance, faithfulness, and context accuracy
• Reduce hallucinations through prompt design, guardrails, and citation-based response generation
• Build and deploy APIs and services using FastAPI or Flask, with monitoring for latency and cost
• Implement caching and optimization strategies to improve performance and efficiency
• Collaborate with product and content teams to define data sources, workflows, and use cases

What Makes You a Great Fit

• 1+ year of hands-on experience building NLP or LLM-based features, with exposure to RAG or retrieval systems
• Strong proficiency in Python and experience working with text-processing pipelines
• Practical knowledge of embeddings, chunking strategies, and document loaders (PDF, HTML, DOC formats)
• Experience with at least one vector database and common similarity or retrieval methods
• Solid understanding of core machine learning and NLP concepts
• Familiarity with modern LLMs (open-source or hosted) and prompt engineering techniques
• Experience deploying AI services and APIs, with an understanding of performance and cost trade-offs
• Ability to collaborate effectively with cross-functional teams in a product-driven environment
• Interest in building scalable, accurate, and user-focused AI systems, especially within EdTech use cases