ML Engineering Manager: Lead AI & Agentic Systems

Argyll Scott; Hong Kong; On-site; HKD 893,000 - 1,116,000; Full time

Job summary

Argyll Scott is seeking a Machine Learning Engineering Manager in Hong Kong to lead the ML engineering team and drive production ML and agentic AI solutions. You will steer technical direction, prioritize delivery, and raise engineering standards across ML deployment and MLOps.

The role requires hands-on leadership, cross-functional collaboration with data scientists and infrastructure, and strong communication across business teams.

Qualifications

• Lead ML engineering team and set technical direction.
• Oversee deployment and operation of ML models, LLM applications and AI agents.
• Strengthen MLOps and agent operations across testing, deployment, monitoring and reliability.
• Review designs and code, guiding engineers on complex problems.
• Partner with data scientists, data architects and infrastructure teams.

Responsibilities

• Lead the ML engineering team, setting technical direction, delivery priorities and engineering standards.
• Oversee the deployment and operation of ML models, LLM applications and AI agents.
• Strengthen MLOps and agent operations practices across testing, deployment, monitoring and reliability.
• Review technical designs and code, providing guidance on complex engineering challenges.
• Partner with data scientists, data architects and infrastructure teams to deliver scalable production solutions.
• Support projects spanning agentic decision support, demand forecasting and AI platforms for business analytics.

Skills

ML engineering

Team leadership

Production ML

MLOps

Communication

LLM applications

Tools

LLM deployment

AI platforms

Job description

Argyll Scott is seeking a Machine Learning Engineering Manager in Hong Kong to lead the ML engineering team and drive production ML and agentic AI solutions. You will steer technical direction, prioritize delivery, and raise engineering standards across ML deployment and MLOps.

The role requires hands-on leadership, cross-functional collaboration with data scientists and infrastructure, and strong communication across business teams.