Applied AI Engineer / Architect at dbs – Guangzhou (DTC) | Jobyssey

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

Applied AI Engineer / Architect

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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.

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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.