- 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




- Solid 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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📍 Noida