- Design, develop, and implement MLOps pipelines for the continuous deployment and integration of machine learning models.

- Collaborate with data scientists and engineers to understand model requirements and optimize deployment processes.

- Automate the training, testing and deployment processes for machine learning models.

- Continuously monitor and maintain models in production, ensuring optimal performance, accuracy and reliability.

- Implement best practices for version control, model reproducibility and governance.

- Optimize machine learning pipelines for scalability, efficiency and cost effectiveness.

- Troubleshoot and resolve issues related to model deployment and performance.

- Ensure compliance with security and data privacy standards in all MLOps activities.

- Keep up-to-date with the latest MLOps tools, technologies and trends.

- Provide support and guidance to other team members on MLOps practices.

Required skills and experience :

- 3- 5 years of experience in MLOps, DevOps or a related field.

- Bachelor's degree in Computer Science, Data Science or a related field.

- Strong understanding of machine learning principles and model lifecycle management.

- Proficiency in programming languages such as Python, with hands-on experience in machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.

- Experience with cloud platforms like AWS, Azure, bare metal server and their respective machine learning services.

- Familiarity with containerization and orchestration tools such as Docker and Kubernetes.

- Knowledge of CI/CD pipelines, automation tools and version control systems like Git.

- Strong problem-solving skills and ability to troubleshoot complex issues.

- Experience with monitoring tools and practices for model performance in production.

- Ability to work collaboratively in cross-functional teams.