Remote MLOps Engineer: Deploy & Scale AI Pipelines

Pt. Air Kampar; Indonesia; Remote; IDR 1,080,886,000 - 1,621,329,000; Full time

Benefits offered by this job

Highly competitive salary and rewards

Fully remote work arrangement

Health, dental, and vision insurance

Budget for professional development

Work with globally distributed AI team

Job summary

Pt. Air Kampar is seeking a skilled Machine Learning Operations (MLOps) Engineer to join their fully remote team. The role focuses on bridging ML development and IT operations, deploying and maintaining ML models in production, and automating pipelines.

Responsibilities include building CI/CD for ML, automating deployment and monitoring, and collaborating with data scientists and software engineers. Remote work with global collaboration and strong emphasis on scalable ML infrastructure.

Qualifications

• Bachelor's degree in CS/Engineering or related field.
• Proven experience in MLOps, DevOps, or similar automation roles.
• Strong scripting skills (Python) and containerization (Docker, Kubernetes).
• Experience with cloud ML services (AWS/Azure/GCP).
• Familiarity with ML frameworks and deployment strategies.
• Ability to work independently in a remote setting.

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

Python scripting

CI/CD pipelines

Remote work

Education

Bachelor's degree in CS/Engineering

Tools

Docker

Kubernetes

AWS

Azure

GCP

Job description

Pt. Air Kampar is seeking a skilled Machine Learning Operations (MLOps) Engineer to join their fully remote team. The role focuses on bridging ML development and IT operations, deploying and maintaining ML models in production, and automating pipelines.

Responsibilities include building CI/CD for ML, automating deployment and monitoring, and collaborating with data scientists and software engineers. Remote work with global collaboration and strong emphasis on scalable ML infrastructure.