RAG Engineers + AI Developers

Accountabilities:: Design and develop end-to-end RAG pipelines covering document ingestion, OCR processing, semantic chunking, metadata extraction, embedding generation, retrieval, and response generation. Build and optimize vector and hybrid search solutions using technologies such as Pinecone, Qdrant, Weaviate, and OpenSearch. Implement advanced retrieval strategies, including query rewriting, multi-query expansion, hybrid keyword-vector search, and cross-encoder reranking. Develop robust document processing and knowledge ingestion workflows capable of handling complex and unstructured enterprise data. Integrate RAG and retrieval components into customer-facing conversational interfaces, enterprise search platforms, and other AI-powered applications. Establish automated evaluation and benchmarking frameworks to measure retrieval performance, context precision, answer relevance, faithfulness, and overall system quality. Continuously optimize retrieval accuracy, system latency, scalability, and reliability in production environments. Investigate and resolve issues across unstructured data pipelines, embeddings, retrieval systems, and LLM-powered applications. Requirements: 3+ years of hands-on experience developing production-grade RAG systems, semantic search solutions, NLP applications, or closely related AI systems. Strong commercial experience working with vector databases and modern embedding models, with practical knowledge of vector and hybrid search architectures. Strong Python development skills and experience with frameworks such as LangChain, LlamaIndex, or comparable/custom retrieval frameworks. Solid understanding of Large Language Models, embeddings, semantic search, information retrieval, document processing, and RAG architecture. Experience implementing and evaluating advanced retrieval techniques such as query expansion, hybrid retrieval, reranking, and relevance optimization. Strong analytical and problem-solving skills, particularly in evaluating retrieval quality, improving latency, and debugging complex unstructured-data workflows. Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related discipline. Ability to work independently in a technically demanding environment and collaborate effectively with AI, data, and engineering teams. Experience: approximately 3–7 years, with mid-to-senior-level expertise in relevant AI/RAG development. Benefits: Annual compensation of approximately INR 2,400,000–3,800,000 CTC , depending on experience and skills. Full-time employment opportunity. Remote-friendly working model with flexibility across India. Opportunity to work on advanced AI, RAG, semantic search, and enterprise knowledge systems. Exposure to modern AI frameworks, vector databases, Large Language Models, and retrieval technologies. Location flexibility including Bangalore, Pune, hybrid, or remote across India . Opportunity to contribute to production-grade AI applications with direct impact on search quality, accuracy, and user experience.