Remote MLOps Engineer: Deploy, Scale & Monitor Models

PT Japfa Comfeed Indonesia Tbk

Banyuwangi

Remote

IDR 350,000,000 - 700,000,000

Full time

3 days ago
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Job summary

PT Japfa Comfeed Indonesia Tbk is seeking a skilled Machine Learning Operations (MLOps) Engineer to join a fully remote, high-performing team. You will bridge ML development and IT operations, deploying, monitoring, and maintaining models in production.

You will design and automate ML pipelines, manage infrastructure, and implement best practices for scalability and efficiency. This is an excellent opportunity to drive AI excellence from anywhere and collaborate with data scientists and software

Qualifications

  • Bachelor's degree in Computer Science, Engineering, or related technical field.
  • Proven experience in MLOps, DevOps, or a similar role focused on automation and infrastructure.
  • Strong proficiency in Python and containerization technologies (Docker, Kubernetes).

Responsibilities

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

Skills

MLOps
DevOps
Python
Docker
Kubernetes
AWS
Azure
GCP
ML frameworks

Education

Bachelor's degree in Computer Science or Engineering

Tools

Docker
Kubernetes

Job description

PT Japfa Comfeed Indonesia Tbk is seeking a skilled Machine Learning Operations (MLOps) Engineer to join a fully remote, high-performing team. You will bridge ML development and IT operations, deploying, monitoring, and maintaining models in production.

You will design and automate ML pipelines, manage infrastructure, and implement best practices for scalability and efficiency. This is an excellent opportunity to drive AI excellence from anywhere and collaborate with data scientists and software

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