Description

You will build and maintain the infrastructure for deploying, monitoring, and scaling machine learning models in production.

This role is on-site.

Responsibilities

• Design and implement MLOps pipelines for model training, testing, deployment, and monitoring.
• Automate end-to-end ML workflows, including data preprocessing, validation, and deployment.
• Monitor and optimize deployed ML models for performance, scalability, and reliability.
• Collaborate with data science and engineering teams to integrate ML models into production.
• Maintain documentation for MLOps processes and best practices.

Required Skills

• 5+ years of professional experience in ML operations.
• Strong programming proficiency in Python or Bash.
• Experience with Docker and Kubernetes for containerization and orchestration.
• Familiarity with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Hands-on experience with MLOps tools including MLflow and Kubeflow.
• Cloud experience using AWS, Azure, or GCP.
• Ability to apply CI/CD practices to ML workflows.

Key Skills

Education

Any Graduate