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LangChain has emerged as the leading framework for building applications powered by large language models, providing tools to chain LLM calls, integrate external data sources, and create intelligent agents. As generative AI transforms software development, LangChain enables developers to build practical LLM applications beyond simple chatbots. Companies hire LangChain developers for building RAG (Retrieval-Augmented Generation) systems, conversational AI applications, document processing pipelines, AI agents, and LLM-powered features in existing products. LangChain developers work with vector databases, embedding models, prompt engineering, tool calling, and integration with LLM providers like OpenAI and Anthropic. The framework's modular design supports complex chains of reasoning and data retrieval. LangChain developer salaries range from $120k to $200k+ reflecting the explosive demand for AI application development. The field is evolving rapidly with new patterns like multi-agent systems and structured outputs emerging regularly. Companies across every industry are investing in LLM-powered features. LeetHire connects AI application developers with companies building the next generation of intelligent software through anonymous hiring.
LangChain is used for building LLM-powered applications including chatbots, RAG systems for question answering over documents, AI agents that use tools, document processing pipelines, and any application that needs to chain multiple AI operations together.
You don't need deep ML knowledge to use LangChain, but understanding LLM concepts (tokens, embeddings, prompting) is essential. LangChain abstracts away model training — you focus on application logic, data retrieval, and prompt engineering.
LangChain and AI application developers earn $120k to $200k+ annually. The demand for developers who can build production LLM applications far exceeds supply. Senior AI engineers building RAG systems and AI agents command $150k-$220k+.
LangChain is the most popular framework with the largest community. Alternatives include LlamaIndex (focused on RAG), Semantic Kernel (Microsoft), and direct SDK usage. LangChain's breadth and ecosystem make it a strong default choice.