AI Engineer (AI Agent）

Key ResponsibilitiesDesign, develop and maintain AI Agent applications powered by Large Language Models (LLMs), including intelligent Q&A, task planning, tool calling, workflow orchestration, multi-agent collaboration and long-term memory.Design technical solutions and build production-ready AI applications using leading LLMs and Agent frameworks, covering solution architecture, prototyping, implementation, testing, deployment and continuous optimisation.Develop and optimise key AI capabilities including: Prompt Engineering, Retrieval-Augmented Generation (RAG), Enterprise Knowledge Base, Function Calling, Model Context Protocol (MCP) integrationsIntegrate LLM capabilities with existing enterprise applications, business systems, databases and third-party APIs to automate and enhance business workflows.Establish evaluation and monitoring mechanisms for AI applications, continuously improving: task completion rate, response accuracy, system reliability, latency, model inference costParticipate in AI product planning, system architecture design, API design, technical reviews and end-to-end project delivery.Keep up to date with the latest developments in LLMs, AI Agents and related technologies, conducting technical research, proof-of-concepts and production implementation.Collaborate closely with Product Managers, AI Scientists, Backend Engineers and business stakeholders to independently deliver core product features.RequirementsEducationBachelor's degree or above in Computer Science, Artificial Intelligence, Software Engineering, Data Science or a related discipline.ExperienceMinimum 5 years of software engineering or backend development experience with strong software engineering fundamentals and coding best practices.Strong proficiency in Python.Solid understanding of: data structures, oriented design, design patterns, concurrent programming, RESTful API development, exception handlingExperience developing backend services using FastAPI, Flask, Django or other modern Python frameworks.AI / LLM ExperienceHands-on experience building LLM-powered applications using one or more of the following:OpenAI, Claude, Gemini, Qwen, DeepSeek, or equivalent commercial/open-source models.Strong understanding of:AI Agents, Prompt Engineering, Retrieval-Augmented Generation (RAG), Function Calling, Model Context Protocol (MCP), Workflow orchestrationExperience with one or more AI frameworks/platforms, such as:LangChain, LangGraph, LlamaIndex, AutoGen, Dify, FastGPT, CozeEngineering SkillsExperience with Git, Docker, Linux and CI/CD pipelines.Familiarity with production deployment, monitoring and troubleshooting.Experience working with relational, vector or search databases, including one or more of:PostgreSQL, MySQL, Redis, Milvus, Elasticsearch, FAISSPreferred QualificationsCandidates with one or more of the following will be highly regarded:Production experience building AI Agents, enterprise knowledge bases, intelligent assistants or multi-agent systems.Experience in LLM fine-tuning, embedding models, reranking, model evaluation, inference optimisation or model serving.Experience with microservices, distributed systems, cloud-native architecture and high-concurrency backend systems.Industry experience in Energy Storage (BESS), Power & Energy, Industrial IoT, Smart Energy or Enterprise Digitalisation.Soft SkillsStrong analytical thinking and problem-solving skills.Ability to independently own and deliver end-to-end technical modules.Excellent communication and cross-functional collaboration skills.Ability to read and understand English technical documentation.