RAG Engineer / AI Developer
New
J
JobgetherArtificial Intelligence
Remote-friendly working model with flexibility across India.Full-TimeSenior
Salary2,400,000 - 3,800,000 INR per year
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Job Details
- Experience
- 3–7 years
- Required Skills
- PythonNLPLLMLangChain
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.
Responsibilities
- 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.
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