Engineering Manager – AI Engineering & LLM Systems

ASUS GLOBAL PTE. LTD.Bedok, Singaporemycareersfuturepublisert 02.11.2026
Må ha:AzureGoogle CloudDockerKubernetesCloudQA/TestAIHealthTechLead

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.

Key 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.

Requirements:

  • 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)