LLM Engineer

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  Work with LLMs such as GPT, Llama, Gemini, and other open-source or commercial models.
  
  

  
  


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  Develop and optimize prompts to improve model accuracy and response quality.
  
  

  
  


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  Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.
  
  

  
  


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  Implement NLP techniques for text processing, summarization, classification, and information extraction.
  
  

  
  


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  Integrate LLMs with applications using APIs and frameworks such as LangChain or LlamaIndex.
  
  

  
  


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  Work with vector databases such as FAISS, Pinecone, Chroma, or Weaviate.
  
  

  
  


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  Evaluate model performance, reduce hallucinations, and optimize response latency and cost.
  
  

  
  


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  Collaborate with data scientists, software engineers, and product teams to build AI-powered solutions.
  
  

  
  


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  Deploy and monitor LLM applications in cloud environments.
  
  

  
  

Requirements

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  Strong knowledge of Python programming.
  
  

  
  


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  Understanding of Large Language Models (LLMs) and Generative AI.
  
  

  
  


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  Knowledge of Natural Language Processing (NLP) and prompt engineering.
  
  

  
  


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  Experience with LangChain, LlamaIndex, or similar frameworks.
  
  

  
  


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  Understanding of RAG architecture, embeddings, and semantic search.
  
  

  
  


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  Familiarity with vector databases such as FAISS, Pinecone, or Chroma.
  
  

  
  


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  Knowledge of REST APIs and application integration.
  
  

  
  


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  Basic understanding of machine learning and deep learning concepts.
  
  

  
  


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  Familiarity with Git and cloud platforms such as AWS, Azure, or Google Cloud is an advantage.