AI/ML Platform Engineer — Cloud, Kubernetes & MLOps

Adecco Portugal; Hong Kong; On-site; HKD 500,000 - 900,000; Full time

Job summary

Adecco Portugal is seeking an AI/ML platform engineer to develop and maintain AI/ML platforms and services in a collaborative team. You will build integrations with internal and external APIs, support monitoring, deployment, and performance optimization, and work with architects, engineers and data scientists to deliver scalable AI solutions.

You will also support the deployment and operationalization of machine learning models, leveraging Docker, Kubernetes and modern DevOps practices to ensure

Qualifications

• Degree in Computer Science, Software Engineering, or related discipline.
• 3+ years of AI/ML platform development experience.
• Strong programming skills in Python.
• Solid experience in software engineering and system design.
• Hands-on exposure to Docker, Kubernetes, Terraform, Git, Jenkins, and MLOps/DataOps practices.
• Experience with cloud platforms (AWS, Azure, or AliCloud) is an advantage.
• Strong communication and problem-solving skills.

Responsibilities

• Develop and maintain AI/ML platforms and services.
• Build integrations with internal and external APIs.
• Support platform monitoring, deployment, and performance optimization.
• Collaborate with architects, engineers, and data scientists to deliver scalable AI solutions.
• Support the deployment and operationalization of machine learning models.

Skills

Python

Docker

Kubernetes

Terraform

Git

Jenkins

MLOps

Data pipelines

Education

Degree in Computer Science / Software Engineering

Tools

Docker

Kubernetes

Terraform

Git

Jenkins

Job description

Adecco Portugal is seeking an AI/ML platform engineer to develop and maintain AI/ML platforms and services in a collaborative team. You will build integrations with internal and external APIs, support monitoring, deployment, and performance optimization, and work with architects, engineers and data scientists to deliver scalable AI solutions.

You will also support the deployment and operationalization of machine learning models, leveraging Docker, Kubernetes and modern DevOps practices to ensure