Engineering Manager – AI Engineering & LLM Systems

ASUS GLOBAL PTE. LTD.; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

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

You will oversee design, development, deployment, and continuous improvement of clinical AI systems and agentic capabilities. You will collaborate with AI Research, Product, and Software Engineering to translate research into scalable, production-grade solutions, implementing best practices and cloud-native workflows.

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.
• Hands-on experience with LLMs and Generative AI development.
• Experience with model fine-tuning and domain adaptation.
• Experience with RAG and knowledge retrieval systems.
• Familiarity with agentic AI frameworks and orchestration.
• Proficient in PyTorch and Hugging Face.
• Experience with Docker and Kubernetes for cloud-native deployments.
• Azure or GCP cloud experience.

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

LLMs & Gen AI

Domain adaptation

Evaluation & benchmarking

RAG retrieval

Agentic AI

Prompt engineering

Tool calling

Tools

PyTorch

Hugging Face

Docker

Kubernetes

Azure

GCP

MLOps

Job description

AICS builds Healthcare AI solutions that improve clinical workflows through large language models, agentic AI, and modern AI engineering.

We are looking for an Engineering Manager to lead a team of Machine Learning Engineers building production-ready LLM applications, AI infrastructure, and clinical AI systems.

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.

Qualifications

Required Experience

• 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.

Technical Expertise

Candidates should have practical, hands-on experience with most of the following:

• Large Language Models (LLMs) and Generative AI application development
• Model fine-tuning or adaptation for domain-specific use cases
• LLM evaluation and benchmarking
• RAG and knowledge retrieval systems
• Agentic AI frameworks and workflow orchestration
• Prompt engineering and structured tool calling
• PyTorch and Hugging Face ecosystem
• AI deployment, inference optimization, and MLOps
• Cloud-native engineering (Docker, Kubernetes, Azure or GCP)