Description

You will deploy, monitor, and maintain machine learning models in production environments.

Responsibilities

• Design and deploy scalable ML pipelines using Kubeflow and Airflow.
• Implement DevOps practices to streamline the software development lifecycle.
• Deploy and monitor ML models using TensorFlow Serving, Prometheus, and Grafana.
• Integrate and manage model versioning and experiment tracking with MLflow.
• Develop and maintain reliable CI/CD pipelines for code and model deployment.

Required Skills

• 3–5 years of experience in MLOps, DevOps, or software development with a focus on automation and scalability.
• Strong understanding of containerization (Docker, Kubernetes).
• Expertise in cloud platforms including AWS, Azure, or GCP.
• Proficiency in scripting languages such as Python or Bash.
• Experience with CI/CD tools like Jenkins or GitLab CI.
• Familiarity with ML pipeline tools, specifically Kubeflow, Airflow, and MLflow.
• Knowledge of monitoring and logging tools like Prometheus and Grafana.
• Experience with Terraform for infrastructure provisioning.
• Working knowledge of TensorFlow.

Key Skills

Education

Any Graduate