LLM Engineer

Responsibilities

• Work with LLMs such as GPT, Llama, Gemini, and other open-source or commercial models
• Develop and optimize prompts to improve model accuracy and response quality
• Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models
• Implement NLP techniques for text processing, summarization, classification, and information extraction
• Integrate LLMs with applications using APIs and frameworks such as LangChain or LlamaIndex
• Work with vector databases such as FAISS, Pinecone, Chroma, or Weaviate
• Evaluate model performance, reduce hallucinations, and optimize response latency and cost
• Collaborate with data scientists, software engineers, and product teams to build AI-powered solutions
• Deploy and monitor LLM applications in cloud environments

Requirements

• Strong knowledge of Python programming
• Understanding of Large Language Models (LLMs) and Generative AI
• Knowledge of Natural Language Processing (NLP) and prompt engineering
• Experience with LangChain, LlamaIndex, or similar frameworks
• Understanding of RAG architecture, embeddings, and semantic search
• Familiarity with vector databases such as FAISS, Pinecone, or Chroma
• Knowledge of REST APIs and application integration
• Basic understanding of machine learning and deep learning concepts
• Familiarity with Git and cloud platforms such as AWS, Azure, or Google Cloud is an advantage