Responsibilities:

Deploy and maintain machine learning models in development and production environments. Build and maintain CI/CD pipelines for ML applications. Work with Git, Docker, Kubernetes , and cloud platforms such as AWS/Azure/Google Cloud Platform. Automate model training, testing, deployment, and monitoring workflows. Monitor model performance, application logs, and infrastructure. Manage ML pipelines and datasets. Collaborate with data scientists, ML engineers, and software developers. Troubleshoot deployment and infrastructure issues. Follow best practices for version control, security, and reproducible ML workflows.

Basic Skills Required:

Python and Linux fundamentals. Git/GitHub. Docker basics. CI/CD concepts (Jenkins, GitHub Actions, GitLab CI, etc.). Basic Kubernetes knowledge. Basic cloud knowledge (AWS/Azure/Google Cloud Platform).

Understanding of ML lifecycle:

data → training → validation → deployment → monitoring . Basic knowledge of tools such as MLflow, Airflow, or Kubeflow is a plus.