Machine Learning Engineering Manager - MLOps & GenAI (Hong Kong)
An exceptional opportunity for a Machine Learning Engineering Manager to join a leading global enterprise in Hong Kong. You will lead a distributed engineering team, driving the production deployment, scaling, and operational excellence of machine learning, Generative AI (GenAI), and agentic AI architectures across regional business units.
about the company.
Our client is a market-leading global retail and enterprise group in Hong Kong operating an expansive regional footprint. Through its central data science and technology innovation hub, they invest heavily in modernising enterprise engineering practices, scaling AI platforms, and building resilient MLOps and LLMOps infrastructures to drive continuous business transformation.
about the team.
You will lead a high-performing, geographically distributed Machine Learning Engineering (MLE) team across Hong Kong and Vietnam. Reporting to the Head of Data Science, you will collaborate closely with Data Scientists, Programme Managers, Enterprise Architecture, and IT infrastructure teams in a fast-paced, tech-forward environment.
about the job.
- Lead end-to-end MLE delivery across multiple AI projects, from technical planning through deployment, monitoring, and production support.
- Own technical milestones, delivery dependencies, resource planning, and solution reliability across ML, GenAI, and agentic AI systems.
- Drive engineering transformation by establishing standardised deployment workflows, reusable components, and automated testing.
- Enhance CI/CD, MLOps, LLMOps, observability, and release management practices across cloud and data platforms.
- Guide architectural decisions, conduct code and design reviews, and establish engineering governance across the MLE function.
- Manage, coach, and develop a distributed engineering team across Hong Kong and Vietnam, building a culture of technical ownership.
- Identify recurring infrastructure bottlenecks and partner with platform teams to resolve constraints and optimise deployment lead times.
- Manage technical discussions, cross-team dependencies, and delivery trade-offs with both technical and executive stakeholders.
skills & experience required.
- Degree in Computer Science, Software Engineering, Data Science, or a related quantitative technical discipline.
- At least 1 year of formal people-management experience, with demonstrated ability to lead, coach, and grow software or machine learning engineers.
- Proven technical background in machine learning engineering, software engineering, or AI platform architecture.
- Hands-on experience deploying, operating, and maintaining production ML, GenAI, or agentic AI applications.
- Strong understanding of MLOps, LLMOps, CI/CD, containerisation (Kubernetes/Docker), and cloud infrastructure (Azure or Databricks).
- Demonstrated ability to evaluate system architectures, manage technical dependencies, and establish engineering standards.
- Experience managing distributed teams across multiple locations or regional cross-functional IT environments is advantageous.
- Fluency in English is required; proficiency in Cantonese or Mandarin is an advantage.
