Data Analytics & ML Ops Engineer
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Summary
Build and run production analytics and machine-learning pipelines: preparing data, deploying and orchestrating models, and monitoring performance and data drift for a telco employer in Indonesia. Requires 3-6 years in data engineering, analytics engineering, or MLOps with cloud-based ML deployment experience.
- Develop and manage analytics and ML pipelines covering data preparation, model deployment, and orchestration in production environments.
- Operationalize machine learning models by integrating models into data platforms, applications, and business workflows.
- Monitor model performance, data drift, and pipeline reliability to ensure stability, accuracy, and continuous improvement.
- Collaborate with data scientists and data engineers to standardize deployment, versioning, and experimentation practices.
- Strengthen MLOps and analytics engineering standards including automation, documentation, and operational controls.
Requirements:
- Bachelor’s Degree in Computer Science, Data Engineering, Information Systems, or related fields.
- Master’s Degree in Data Engineering or AI is an advantage.
- 3–6 years of experience in data engineering, analytics engineering, or MLOps roles.
- Experience with cloud-based analytics platforms and ML deployment in production environments
- Experience in Telco industry is a plus.
