- 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 Lang Chain or Llama Index.

- 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 Lang Chain, Llama Index, 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 (India)
🏢 Training Basket
📍 India