Remote MLOps Engineer: Automate & Scale ML Pipelines

Hembusan; Cikarang; Remote; IDR 300,000,000 - 600,000,000; Full time

Benefits offered by this job

Fully remote work

Competitive salary

Health, dental, and vision insurance

Professional development budget

Global distributed team

Job summary

Nestlé Indonesia is seeking a Machine Learning Operations (MLOps) Engineer to join a fully remote, high-performing team. The role focuses on bridging ML development and IT operations, deploying and maintaining ML models with scalable pipelines and infrastructure, and collaborating with data scientists and engineers.

The ideal candidate will have a solid MLOps/DevOps background, strong Python scripting, Docker/Kubernetes skills, and experience with AWS/Azure/GCP ML services to enable reliable,

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field.
• Proven experience in MLOps/DevOps or similar role focused on automation and infrastructure.
• Strong scripting (e.g., Python) and containerization (Docker, Kubernetes).
• Experience with cloud platforms (AWS, Azure, GCP) and ML deployment strategies.

Responsibilities

• Design, implement, and maintain CI/CD pipelines for ML models.
• Automate deployment, scaling, and monitoring of ML models in production.
• Develop infrastructure and tooling to support the ML lifecycle.
• Collaborate with data scientists and software engineers to troubleshoot production issues.
• Implement robust monitoring, logging, and alerting for ML systems.
• Ensure security, reliability, and performance of ML infrastructure.

Skills

Python

MLOps

DevOps mindset

Docker

Kubernetes

Cloud platforms

Model deployment

Problem solving

Education

Bachelor's degree

Tools

Docker

Kubernetes

Cloud services

Job description

Nestlé Indonesia is seeking a Machine Learning Operations (MLOps) Engineer to join a fully remote, high-performing team. The role focuses on bridging ML development and IT operations, deploying and maintaining ML models with scalable pipelines and infrastructure, and collaborating with data scientists and engineers.

The ideal candidate will have a solid MLOps/DevOps background, strong Python scripting, Docker/Kubernetes skills, and experience with AWS/Azure/GCP ML services to enable reliable,