Applied AI Engineer / Architect

Job Responsibilities

1. Leadership & Stakeholder Management Leadership: Act as the subject matter expert for Agentic engineering. Guide the overarching AI technology stack selection, system architecture design, and long-term engineering excellence. User & Requirement Alignment: Collaborate closely with product management and end-users to translate complex business workflows and user needs into concrete multi-agent requirements. Team Mentorship: Mentor and upskill engineering team members on LLM architectures, prompt engineering, asynchronous backend development, and AI engineering best practices.
2. Multi-Agent & LLM Engineering Agent System Design: Design and deploy Agents from 0 to
3. Own the architecture design, Tool/Function Calling implementations, multi-Agent collaboration protocols, and complex Workflow orchestrations. Framework Implementation: Leverage LLM ecosystems and SDKs to build robust corporate solutions using MCP and Agentic Workflows. AI Evaluation: Build and construct automated evaluation pipelines to validate non-deterministic agent behaviors, optimize decision-making accuracy.
4. Backend & Distributed Systems Infrastructure Production Services: Architect, develop, test, and deploy highly concurrent, high-availability, production-grade Web Services. Independently complete backend service infrastructure. System Optimization: Build and optimize high-performance distributed systems, driving system performance optimization and engineering excellence across the entire stack. DevOps & Deployment: Utilize containerization technologies like Docker and Open. Shift to complete application deployment, scaling, and daily operations. Job Requirements:
5. Experience & Track Record Experience: Approximately 10 years of professional working experience. AI Focus: The latest 4–5 years must be specifically dedicated to the AI domain, with a proven track record in LLM and Agent technologies Project Track Record: Must have 3+ years of hands-on AI-related experience, with active participation in at least 3 real-world production-grade deployment projects. At least 1 project must be a complex Multi-Agent, Agentic Workflow
6. Technical Skills & Tech Stack Languages & Core Backend: Expertise in Python, advanced asyncio, and FastAPI, with a proven track record of designing high-concurrency, high-availability backend architectures. AI & Multi-Agent Frameworks: Hands-on proficiency with Lang. Graph, CrewAI, Auto. Gen, Lang. Chain, Llama. Index, Google ADK, and Claude SDK LLM Core & Protocols: Deep understanding of model inference, Prompt Engineering, Tool/Function Calling, Model Context Protocol (MCP), and Agentic Workflows. DevOps & Infrastructure: Experience in distributed system development, building/maintaining complete CI/CD pipelines, and using containerization tools like Docker and Open. Shift for deployment and operations. Location: Guangzhou (DTC) Job: Data Technology Schedule: Regular Employee Status: Full time