• 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





• Robust 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.

📌 LLM Engineer (Noida)
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