Machine Learning Engineer - MLOps & Generative AI (Hong Kong)

Randstad Hong Kong Limited; Hong Kong; On-site; HKD 600,000 - 1,200,000; Full time

Job summary

Randstad Hong Kong Limited is seeking a skilled Machine Learning Engineer to productionise ML and Generative AI models in a fast-paced, enterprise environment in Hong Kong.

You will build scalable data pipelines, collaborate with Data Scientists, Data Engineers, and System Architects, and deploy AI solutions across international markets with a focus on reliability and performance.

Qualifications

• 4+ years of experience as a Machine Learning Engineer, Data Engineer, Python Developer, or DevOps Engineer.
• 1 year of experience supporting and maintaining software products or platforms in a live production environment.
• Experience developing and optimising end-to-end ML pipelines from data ingestion to deployment and monitoring.
• Strong data engineering skills with SQL, PySpark, and database performance optimisation.
• Hands-on experience with LLM applications and API integration (Azure OpenAI/OpenAI), including streaming, tool calling, and token tracking.
• Proficiency in Python and its ecosystem (Pandas, NumPy, PyTorch, scikit-learn).
• Experience with containerisation (Docker/Kubernetes), CI/CD, and Git workflows.
• Familiarity with Python API frameworks (FastAPI/Flask) and task queues (Celery).
• Experience with MLOps and observability tools (MLflow, ClearML, Grafana, ELK).
• Fluency in English; Cantonese/ Mandarin is a plus.

Responsibilities

• Design and implement reliable, scalable AI pipelines and systems delivering commercial AI solutions.
• Develop production-grade ML and Generative AI applications prioritising maintainability and architecture.
• Productionise, deploy, and monitor AI models for low latency and high reliability.
• Optimize data pipelines, training workflows, inference processes, and API integrations.
• Lead end-to-end technical initiatives with governance and risk mitigation.
• Collaborate with Data Scientists, Data Engineers, System Architects, and Product Managers to translate specs into ML solutions.
• Contribute hands-on with software teams and third parties, ensuring security and performance.

Skills

Python

SQL

PySpark

Pandas

NumPy

PyTorch

scikit-learn

Docker

Kubernetes

FastAPI

Flask

Celery

MLflow

ClearML

Grafana

ELK

CI/CD

Education

Bachelor Degree

Tools

Azure OpenAI

OpenAI

Docker

Kubernetes

FastAPI

Job description

An exceptional opportunity for a Machine Learning Engineer to join an innovative technology enterprise in Hong Kong. You will productionise machine learning and Generative AI models, build scalable end-to-end data pipelines, and partner with Data Architects, Data Scientists, and Engineers to deploy high-impact, enterprise-grade AI solutions across international markets.

about the company.

Our client is a prominent multinational enterprise in Hong Kong dedicated to driving technological innovation and digital transformation across the region. They invest heavily in scalable cloud systems, machine learning infrastructure, and Generative AI capabilities to deliver resilient, data-driven products across diverse global markets.

about the team.

You will join a high-performing AI and engineering team collaborating closely with System Architects, Data Scientists, Software Developers, and Product Managers. The team culture is engineering-driven, collaborative, and fast-paced, offering a forward-thinking environment to design next-generation machine learning platforms and scale production architectures.

about the job.

• Solve real-world business challenges by designing and implementing reliable, scalable AI pipelines and systems that deliver impactful commercial solutions.

• Develop production-grade machine learning and Generative AI applications adhering to engineering best practices, prioritising maintainability, and architectural flexibility.

• Productionise, deploy, and monitor robust AI models to ensure optimal performance, low latency, and high production reliability.

• Monitor and enhance the performance of AI products by optimising data pipelines, model training workflows, inference processes, and API integrations.

• Own technical initiatives end to end: initiating, defining, planning, mitigating risks, and executing deliverables through governance gates.

• Make hands-on delivery contributions with software development teams and third parties, rigorously testing solutions across security, performance, and integration.

• Collaborate with Data Scientists, Data Engineers, System Architects, and Product Managers to translate technical specifications into innovative machine learning solutions.

• Partner with enterprise architects to design and build next-generation machine learning platforms and MLOps infrastructure.

• Foster an engineering mindset, promote development best practices, and advocate for data-driven decision-making across the organisation.

skills & experience required.

• Bachelor's degree or higher in Computer Science, Data Science, Machine Learning, or a related quantitative technical discipline.

• Minimum 4+ years of experience as a Machine Learning Engineer, Data Engineer, Python Developer, or DevOps Engineer.

• Minimum 1 year of experience supporting and maintaining software products or platforms in a live production environment.

• Demonstrated experience developing and optimising end-to-end ML pipelines, spanning data ingestion to deployment and lifecycle monitoring.

• Strong data engineering capabilities, with solid expertise in SQL, PySpark, and database performance optimisation.

• Hands-on experience in LLM application development and API integration (preferably Azure OpenAI / OpenAI), covering streaming, structured outputs, tool calling, retry handling, rate limits, and token/cost tracking.

• High proficiency in Python and its ecosystem, including Pandas, NumPy, PyTorch, and scikit-learn.

• Proficiency in Python packaging and testing frameworks such as setuptools, unittest, and pytest.

• Solid experience with containerisation (Docker/Kubernetes), CI/CD pipelines, and Git collaborative workflows.

• Hands-on experience with Python API frameworks such as FastAPI or Flask.

• Familiarity with task queues like Celery, including worker management, failure diagnosis, timeout handling, and health monitoring.

• Experience with MLOps practices and observability tooling, such as MLflow, ClearML, Grafana, or ELK.

• Fluency in English is required; proficiency in Cantonese or Mandarin is an advantage.

skills

no additional skills required

qualifications

no additional qualifications required

education

Bachelor Degree