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Job Responsibilities

Application development of AI Agent
Responsible for building enterprise-level intelligent agent applications, such as intelligent data analysis assistants, intelligent reporting systems, knowledge question answering, process automation, and other scenarios.
Master the construction of RAG (Retrieval Augmented Generation) systems, including prompt engineering, vector databases, embeddings, and retrieval optimization strategies (hybrid retrieval, reranking, query reformulation, etc.).
Implement task planning, dialogue management, intent recognition, tool invocation, process engineering, RAG, etc. for intelligent agents based on LangChain, LangGraph, LlamaIndex, and Agent Framework.
Able to independently complete the entire process of an intelligent agent, from design to launch.
Promote continuous optimization of AI Agent in terms of architecture and performance.
Implement A2A (Agent-to-Agent) orchestration, multi-agent collaborative task design, and implementation for intelligent agents.
Building Intelligent BI Capabilities
Automatically convert natural language into data queries (SQL/DSL), and interface with enterprise data APIs and data warehouses.
Build capabilities such as logical analysis, field mapping, indicator definition, exception handling, and result interpretation for intelligent BI.
Collaborate with the data team to optimize data calibers, indicator systems, and knowledge graphs.
Utilize vector retrieval (Milvus/PGVector) + structured data query to build a hybrid RAG
Optimize recall, enhance data context, reduce hallucinations, and improve answer accuracy.

Job Requirements

Major in computer science or related fields is preferred, with a bachelor's degree or above.
Proficient in using AI programming tools (such as Cursor, Copilot, GPT, Qoder, etc.), which can significantly enhance development efficiency.
Proficient in the Python programming language, with experience in designing complex backend system architectures, proficiency in frameworks such as FastAPI, strong software framework and application design capabilities, high-performance architecture design, and a deep understanding of design patterns.
Possess excellent abilities in code writing, debugging, optimization, and engineering thinking; be familiar with the Linux development environment, version control Git, and Docker operations.
Experience in developing RAG applications, question-answering systems, SQL automatic generation, or data analysis assistants.
Familiar with the design patterns of AI Agents: ReAct, Plan-and-Execute, Reflection, etc.
Proficient in the use of vector databases (such as Milvus, Chroma) and traditional databases (PostgreSQL, Sql Server, Mysql, Redis, Elasticsearch);
Familiar with any of the AI agent frameworks: LangChain/LangGraph, LlamaIndex, CrewAI, AutoGen, or Agent Framework.
Proficient in basic front-end development (Vue/React, either) for developing user interfaces or result display pages for intelligent agents.

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AI Application Development Engineer