DBS Bank

Regional bank offering digital banking

Applied AI Engineer / Architect

AI Application Engineering +1Updated on Nov 12

Full-timeGuangzhou, Guangdong Province, ChinaNo salary listedIn-person

About the Job

Requirements

Approximately 10 years of professional working experience.
The latest 4–5 years of experience must be specifically dedicated to the AI domain, with a proven track record in large language model and agent technologies.
At least 3 years of hands-on AI-related experience and active participation in at least 3 production-grade deployment projects, including at least 1 complex multi-agent or agentic workflow project.
Expertise in Python, advanced asyncio, and FastAPI, with a proven track record designing high-concurrency, high-availability backend architectures.
Hands-on proficiency with LangGraph, CrewAI, AutoGen, LangChain, LlamaIndex, Google ADK, and Claude SDK.
Deep understanding of model inference, prompt engineering, tool/function calling, Model Context Protocol (MCP), and agentic workflows.
Experience in distributed system development, building and maintaining complete CI/CD pipelines, and using containerization tools such as Docker and OpenShift for deployment and operations.

Responsibilities

Act as the subject matter expert for agentic engineering and guide AI technology stack selection, system architecture design, and long-term engineering excellence.
Collaborate with product management and end-users to translate business workflows and user needs into concrete multi-agent requirements.
Mentor and upskill engineering team members on large language model architectures, prompt engineering, asynchronous backend development, and AI engineering best practices.
Design and deploy agents from 0 to 1, owning architecture design, tool/function calling implementations, multi-agent collaboration protocols, and complex workflow orchestrations.
Use large language model ecosystems and SDKs to build corporate solutions using MCP and agentic workflows.
Build automated evaluation pipelines to validate non-deterministic agent behaviors and optimize decision-making accuracy.
Architect, develop, test, and deploy highly concurrent, high-availability, production-grade web services; independently complete backend service infrastructure.
Build and optimize high-performance distributed systems, driving system performance optimization and engineering excellence across the entire stack.
Use containerization technologies such as Docker and OpenShift to deploy and scale applications and perform daily operations.

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

About the Company

DBS Bank

Regional bank offering digital banking

DBS Bank provides banking and financial services in Singapore and Southeast Asia, including personal and business banking, loans, deposits, wealth management, and payments. It serves customers through a network of branches and a digital platform that lets people open accounts, transfer funds, borrow, and invest via online and mobile apps. The bank differentiates itself with government-backed origins, rapid regional growth, a strong emphasis on digital banking, and a broad mix of consumer, corporate, and wealth services. Its goal is to be a leading, trusted financial services provider in Asia, supporting customers and economies with reliable banking and sustainable growth.

Company size: 10,001+
Company stage: IPO
Headquarters: Singapore, Singapore
Founded: 1968

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DBS Bank

Applied AI Engineer / Architect

About the Job

Requirements

Responsibilities

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.

About the Company

DBS Bank

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