Applied AI Engineer / Architect

Job description

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 1. 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. 3.

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 OpenShift to complete application deployment, scaling, and daily operations.

Job Requirements

1.

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 2. 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 LangGraph, CrewAI, AutoGen, LangChain, LlamaIndex, 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 OpenShift for deployment and operations.

Location

Guangzhou (DTC)

Job

Data Technology Schedule: Regular Employee Status: Full time