Remote MLOps Engineer: AI CI/CD & Deployment

Cari; Cirebon; Remote; IDR 223,200,000 - 446,400,000; Full time

Benefits offered by this job

Remote work

Job summary

Cari is seeking an experienced Machine Learning Operations (MLOps) Engineer to join a globally distributed, fully remote team. The role focuses on streamlining ML model lifecycles from development to deployment and monitoring, ensuring scalable, reliable AI systems in production.

You will partner with data scientists and software engineers to design and maintain CI/CD pipelines, manage cloud infrastructure, and implement robust monitoring and retraining mechanisms while thriving in a remote,

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field.
• Proven experience in MLOps, DevOps, or related infrastructure roles.
• Strong understanding of ML concepts and workflows.
• Experience with cloud ML services and containers (Docker, Kubernetes).
• Strong scripting skills in Python or Bash and remote collaboration.

Responsibilities

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

Skills

MLOps experience

DevOps experience

Python scripting

Problem solving

Remote collaboration

Education

Bachelor's degree in CS/Engineering

Tools

Docker

Kubernetes

AWS

Azure

GCP

Job description

Cari is seeking an experienced Machine Learning Operations (MLOps) Engineer to join a globally distributed, fully remote team. The role focuses on streamlining ML model lifecycles from development to deployment and monitoring, ensuring scalable, reliable AI systems in production.

You will partner with data scientists and software engineers to design and maintain CI/CD pipelines, manage cloud infrastructure, and implement robust monitoring and retraining mechanisms while thriving in a remote,