The Role
Lead design and delivery of multi-agent LLM systems and agentic workflows. Translate product requirements into architectures, mentor engineers, build production-grade high-concurrency backend services, implement CI/CD and containerized deployments, and create automated evaluation pipelines for agent behaviors.
Summary Generated by Built In
Job Responsibilities:3. Backend & Distributed Systems Infrastructure
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.
- 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.
- 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.
- 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 TechnologySchedule:
RegularEmployee Status:
Full timeSkills Required
- Approximately 10 years of professional working experience
- Latest 4-5 years dedicated specifically to the AI domain with proven LLM and Agent technologies experience
- 3+ years hands-on AI-related experience with participation in at least 3 production-grade deployment projects; at least 1 complex Multi-Agent/Agentic Workflow project
- Expertise in Python, advanced asyncio, and FastAPI for 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 building and optimizing distributed systems, production services, and automated evaluation pipelines for non-deterministic agent behavior
- Experience with CI/CD pipelines and containerization/deployment using Docker and OpenShift
- Leadership, stakeholder management, and mentoring experience to guide engineering teams and align requirements with product stakeholders
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The Company
What We Do
DBS Bank is a leading financial services group in Asia, headquartered in Singapore. It provides a full range of consumer, SME, and corporate banking services. The bank is recognized for its digital innovation, having been named 'World's Best Digital Bank' and 'World's Best Bank' by various publications. It also operates the DBS Foundation, which supports social enterprises and community initiatives, reflecting its commitment to creating impact beyond traditional banking.
