Engineering Manager, AI & LLM Systems

ASUS GLOBAL PTE. LTD.; Singapore; On-site; SGD 180,000 - 320,000; Full time

Job summary

ASUS GLOBAL PTE. LTD. in Singapore seeks an Engineering Manager to lead a team of Machine Learning Engineers building production-ready LLM applications, AI infrastructure, and clinical AI systems.

You will drive design and deployment of scalable AI workflows, manage multi-agent capabilities, and establish best practices across AI development with close collaboration with AI Research, Product, and Software Engineering teams to translate research into robust production products.

Qualifications

• 8+ years of software engineering, machine learning engineering, or AI engineering experience.
• 3+ years leading technical engineering teams.
• Proven experience delivering AI products into production.

Responsibilities

• Lead and grow a high-performing Machine Learning Engineering team.
• Drive the design, development, and deployment of production LLM applications and AI systems.
• Build scalable AI engineering workflows covering model evaluation, experimentation, deployment, and continuous improvement.
• Lead the development of agentic AI capabilities, including multi-agent workflows, tool integration, memory, and orchestration.
• Establish engineering best practices for AI development, software quality, testing, and MLOps.
• Work closely with AI Research, Product, and Software Engineering teams to translate research into production products.
• Continuously evaluate emerging AI technologies and adopt new techniques that improve product quality and engineering productivity.

Skills

Leadership

Team management

Production AI experience

Tools

PyTorch

Hugging Face

Docker

Kubernetes

Azure or GCP

Job description

ASUS GLOBAL PTE. LTD. in Singapore seeks an Engineering Manager to lead a team of Machine Learning Engineers building production-ready LLM applications, AI infrastructure, and clinical AI systems.

You will drive design and deployment of scalable AI workflows, manage multi-agent capabilities, and establish best practices across AI development with close collaboration with AI Research, Product, and Software Engineering teams to translate research into robust production products.