Remote MLOps Engineer: AI Deployment & Pipelines

Japfa; Indonesia; Remote; IDR 240,000,000 - 520,000,000; Full time

Benefits offered by this job

Fully remote work

Competitive salary

Professional development budgets

Job summary

Japfa is seeking a highly skilled Machine Learning Operations (MLOps) Engineer to join a globally distributed team working entirely remotely. You will design, build, and maintain CI/CD pipelines for ML models, and manage infrastructure for training, deployment, and monitoring.

Collaborate with data scientists and IT to deliver scalable, secure ML solutions. The role requires strong ML concepts, Docker, Kubernetes, cloud skills, and proficiency in Python or Bash.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field.
• Proven experience in MLOps, DevOps, or related infrastructure engineering roles.
• Strong understanding of machine learning concepts and workflows.
• Proficiency with cloud platforms (AWS, Azure, GCP) and their ML services.
• Experience with containerization (Docker) and orchestration (Kubernetes).
• Solid scripting skills (e.g., Python, Bash).
• Excellent problem-solving skills and the ability to thrive in a remote, collaborative setting.

Responsibilities

• Design, build, and maintain CI/CD pipelines for machine learning models.
• Implement and manage infrastructure for training, deploying, and serving ML models.
• Develop strategies for model monitoring, performance tracking, and retraining.
• Automate model validation and testing processes.
• Collaborate with data scientists to optimize models for production environments.
• Ensure the scalability, reliability, and security of ML infrastructure.

Skills

MLOps

DevOps

Python

Bash

Cloud fundamentals

Education

Bachelor's degree (CS/Engineering)

Tools

Docker

Kubernetes

Job description

Japfa is seeking a highly skilled Machine Learning Operations (MLOps) Engineer to join a globally distributed team working entirely remotely. You will design, build, and maintain CI/CD pipelines for ML models, and manage infrastructure for training, deployment, and monitoring.

Collaborate with data scientists and IT to deliver scalable, secure ML solutions. The role requires strong ML concepts, Docker, Kubernetes, cloud skills, and proficiency in Python or Bash.