ML Engineer

Kerry Consulting; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Kerry Consulting is seeking an experienced Machine Learning Engineer in Singapore to design, develop, and productionise ML models and services across products and platforms.

You will work at the intersection of ML, software engineering, and cloud infrastructure, bringing experiments to scalable production deployments with robust CI/CD and observability.

Qualifications

• Proven experience building and deploying ML models in production.
• Strong software engineering fundamentals and production ML deployment know-how.
• Experience with MLOps, monitoring, and automated training/inference pipelines.

Responsibilities

• Design, develop, and productionise ML models and services.
• Collaborate with data scientists, software engineers and product teams to integrate ML capabilities.
• Build and maintain MLOps infrastructure and automated ML pipelines, CI/CD for ML, model monitoring.

Tools

Python

PyTorch

TensorFlow

MLflow

Kubeflow

Docker

Kubernetes

AWS

Azure

GCP

CI/CD pipelines

Job description

We are seeking an experienced Machine Learning Engineer to join a fast-growing technology company, building and scaling machine learning capabilities across its products and platforms.

This is an opportunity to work at the intersection of machine learning, software engineering, and cloud infrastructure, taking ML solutions from experimentation through to reliable, scalable production deployment.

Responsibilities

You will design, develop, and productionise machine learning models and services, working closely with data scientists, software engineers, and product teams to integrate ML capabilities into technology products.

You will build and maintain MLOps infrastructure and automated ML pipelines covering model training, testing, deployment, monitoring, and retraining. You will establish CI/CD practices for machine learning, improve model reliability and performance, and develop scalable infrastructure to support models in production.

You will also contribute to ML platform architecture, model serving, observability, and automation, ensuring machine learning systems are scalable, secure, and efficient.

Requirements

We are looking for an experienced Machine Learning Engineer with strong software engineering fundamentals and hands-on experience deploying and operating machine learning models in production.

Strong proficiency in Python and experience with machine learning frameworks such as PyTorch, TensorFlow, or similar technologies are essential. You should have practical MLOps experience, including model deployment, CI/CD, model monitoring, experiment tracking, containerisation, and automated training and inference pipelines.

Experience with Docker, Kubernetes, cloud platforms (AWS, Azure, or GCP), and MLOps technologies such as MLflow, Kubeflow, or equivalent tools will be highly relevant. Experience building scalable APIs, model serving infrastructure, and production ML platforms would also be advantageous.