About the Opportunity
You will be responsible for the core architecture design and technical delivery of an enterprise-grade AI Native engineering platform, driving the deep integration of large language models, AI Agents, knowledge capabilities, and the software development lifecycle. The platform will support intelligent capabilities across requirement analysis, solution generation, coding collaboration, test automation, and environment orchestration. This role focuses on engineering-grade AI implementation in complex R&D scenarios. You are expected to turn model capabilities into reusable, scalable, and governable platform capabilities, and continuously drive the evolution of the engineering platform toward an AI Native architecture.
What You’ll Be Doing
- Lead the overall architecture design and core capability development of the AI Agent platform, including task orchestration, tool invocation, context management, memory mechanisms, and multi-agent collaboration.
- Drive the implementation of AI Native capabilities in engineering workflows, including AI coding, technical solution generation, test case generation, test report generation, and automated environment deployment.
- Build the knowledge augmentation layer for the platform, including the design, integration, and application of RAG, GraphRAG, and knowledge graph capabilities.
- Establish the runtime and governance framework for AI Agents, including execution isolation, permission control, auditability, evaluation, stability, and cost governance.
- Collaborate with product, engineering, QA, and operations teams to standardize, productionize, and scale platform capabilities, continuously improving engineering efficiency and delivery quality.
What We Look For In You
- Bachelor’s degree or above, preferably in Computer Science, Software Engineering, Artificial Intelligence, or related fields.
- 6+ years of experience in backend engineering, platform engineering, or infrastructure engineering, with strong system design and delivery capabilities.
- Proficient in Java / Python, with solid coding skills, architectural thinking, and experience building complex systems.
- Strong understanding of LLM and AI Agent architectures, including task decomposition, tool use, context management, memory, execution loops, and exception handling.
- Hands-on experience with LangChain and LangGraph, and strong familiarity with mainstream AI coding tools or frameworks such as Claude Code, Codex, Cursor, and Copilot.
- Experience in designing or developing RAG / GraphRAG / knowledge graph solutions for enterprise knowledge augmentation and engineering use cases.
- Good understanding of DevOps, CI/CD, Kubernetes, and cloud-native technologies, with strong cross-functional collaboration and execution skills.
Nice to Haves
- Experience contributing to open-source projects related to AI Agents, AI Coding, RAG, or knowledge graphs.
- Experience building enterprise engineering platforms, DevOps platforms, or AI platforms.
- Familiarity with open-source Agent projects such as openclaw, openhands/openharness, or Hermes.