AI Architecture AI架构师

Description

About us Bitget Wallet is the non-custodial crypto wallet designed to make crypto simple and secure for everyone. With over 80 million users, we bring together a full suite of crypto services, including swaps, market insights, staking, rewards, DApp exploration, and payment solutions. Supporting 130+ blockchains and millions of tokens, Bitget Wallet enables seamless multi-chain trading across hundreds of DEXs and cross-chain bridges. Backed by a $300+ million user protection fund, it ensures the highest level of security for users' assets. What you'll do Continuously develop and upgrade the Architecture Team’s core services (API gateways, service registration and discovery, load balancing, etc.) to ensure system stability and scalability Provide highly available, high-concurrency, low-latency technical solutions for various company initiatives and business scenarios to support rapid business iteration Continuously build company-wide technical infrastructure, including shared development tools, code toolkits, and middleware, to enhance R&D efficiency Define the technical architecture for the AI application platform, covering LLM integration, agent orchestration, RAG, function calling, and multi-agent collaboration Plan AI middleware (vector databases, model gateways, prompt management, and evaluation and monitoring systems) Drive the implementation of AI to enhance R&D efficiency (code generation, intelligent testing, intelligent operations and maintenance, fault diagnosis,

Responsibilities

• Continuously develop and upgrade the Architecture Team’s core services (API gateways, service registration and discovery, load balancing, etc.) to ensure system stability and scalability
• Provide highly available, high-concurrency, low-latency technical solutions for various company initiatives and business scenarios to support rapid business iteration
• Continuously build company-wide technical infrastructure, including shared development tools, code toolkits, and middleware, to enhance R&D efficiency
• Define the technical architecture for the AI application platform, covering LLM integration, agent orchestration, RAG, function calling, and multi-agent collaboration
• Plan AI middleware (vector databases, model gateways, prompt management, and evaluation and monitoring systems)
• Drive the implementation of AI to enhance R&D efficiency (code generation, intelligent testing, intelligent operations and maintenance, fault diagnosis, etc.)
• Lead the API-ization and platformization of AI capabilities to lower the barrier to entry for business teams
• Establish technical standards and specifications (including AI engineering guidelines) to drive technology implementation and ensure code quality and system performance

Requirements

• Bachelor’s degree or higher in Computer Science, Software Engineering, or a related field
• At least 5 years of backend development experience, including at least 3 years of Go development experience; prior experience in a technical lead or architect role
• Hands-on experience building high-concurrency systems, with a deep understanding and practical experience with microservices architecture
• Proficiency in distributed system design principles, and familiarity with common system architecture patterns and best practices
• Extensive practical experience with caching design, database performance optimization, and middleware such as message queues
• In-depth understanding of the capabilities and engineering practices of mainstream LLMs (GPT, Claude, Llama, Qwen, etc.)
• Practical experience implementing AI application architectures such as RAG and Agents
• Strong communication skills and a collaborative spirit, with the ability to drive the comprehensive implementation of technical requirements across teams