LLM Engineer
Summary
Builds and ships LLM-powered applications: writing and optimizing prompts, creating RAG pipelines with vector databases, applying NLP techniques, and deploying/monitoring models in the cloud. Core stack is Python with GPT/Llama/Gemini models, LangChain/LlamaIndex, and FAISS/Pinecone/Chroma.
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.