AI Engineer Legal-tech

【Mission & Core Challenges】 - Take responsibility for the design, development, and operation of AI-native features centered on RAG, LLMs, and AI agents, ensuring a 'truly usable AI experience' by delving into UI/UX and business workflows. - Lead model selection, inference configuration, RAG design, and agent design tailored to specific use cases, achieving a balance between quality and speed. - Conduct operational design—including evaluation, monitoring, log design, and cost optimization—to continuously improve and maintain a reliable AI experience. 【Responsibilities】 - Design and implement highly stable agent LLM workflows for production environments. - Build agent execution infrastructure by integrating with external services and internal APIs. - Evaluate and select suitable external models, frameworks, and services. - Define user needs through requirement gathering and collaborate with other departments to design optimal agent solutions. - Establish testing frameworks and monitoring systems to evaluate AI agent performance and visualize key metrics. - Establish development processes and quality standards through code reviews and technical documentation. - Identify technical challenges to ensure the accuracy, reliability, performance, and scalability of AI systems and drive continuous improvements. 【Required Skills & Qualifications】 - 5+ years of development experience in Python, TypeScript, or Go. - Experience developing applications using LLM APIs (e.g., OpenAI, Anthropic, Google Gemini, Mistral). - Practical experience building AI agents (e.g., LangChain, CrewAI, OpenAI Agent SDK, Google ADK, MCP) and performing context engineering. - Experience designing and implementing RAG (embeddings, vector databases, and search algorithms). - Experience improving generation accuracy through prompt optimization and the design of evaluation metrics. - Basic knowledge of natural language processing and experience building data pipelines. - Language proficiency equivalent to JLPT N1. 【Preferred Skills】 - Experience developing search platforms using Elasticsearch, OpenSearch, Vespa, and similar technologies. - Experience designing systems that integrate knowledge graphs, structured data, and RAG. - Experience designing and operating systems in cloud environments such as AWS, Google Cloud, and Azure. - Experience preprocessing and normalizing unstructured text, such as customer emails, FAQs, and product documentation.