AI Native Platform Architect Headline

OKX is one of the world's largest cryptocurrency exchanges and the developer of OKX Wallet, giving millions access to crypto trading and decentralized applications. Backed by its Proof of Reserves system, OKX serves both retail traders and hundreds of large institutions seeking access to global crypto markets.

This role is responsible for the core architecture design and technical delivery of an enterprise-grade AI Native engineering platform, driving 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. 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.

• 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 implementation of AI Native capabilities in engineering workflows: AI coding, technical solution generation, test case generation, test report generation, and automated environment deployment.
• Build the knowledge augmentation layer for the platform, including RAG, GraphRAG, and knowledge graph design, integration, and application.
• Establish the runtime and governance framework for AI Agents, covering 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.

• 8+ years of experience in backend engineering, platform engineering, or infrastructure engineering with strong system design capabilities.
• Proficient in Java/Python with solid coding skills, architectural thinking, and experience building complex systems.
• Strong understanding of LLM and AI Agent architectures: task decomposition, tool use, context management, memory, execution loops, and exception handling.
• Hands-on experience with LangChain and LangGraph; familiarity with AI coding tools such as Claude Code, Codex, Cursor, and Copilot.
• Experience designing or developing RAG/GraphRAG/knowledge graph solutions for enterprise knowledge augmentation.
• Good understanding of DevOps, CI/CD, Kubernetes, and cloud-native technologies.