ML Ops

Main Roles and Responsibilities

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

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

3. Run machine learning feature engineering tests and improve models based on those tests.

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

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

6. Implement best practices for version control model reproducibility and governance.

7. Optimize machine learning pipelines for scalability efficiency and costeffectiveness.

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

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

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

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

12. Select appropriate data sets and representation methods perform statistical analysis and verify data quality

13. identify differences in data distribution that affects model performance

14. Develop machine learning apps according to client requirements and extend machine learning libraries

Required skills and experience

experience in Azure Azure machine learning MLOps DevOps or a related field.

Strong understanding of machine learning principles and model lifecycle management.

Proficiency in programming languages such as Python with handson experience in machine learning frameworks like TensorFlow PyTorch or Scikitlearn.

Experience with cloud platforms like Azure AWS or 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 problemsolving skills and ability to troubleshoot complex issues.

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

Ability to work collaboratively in crossfunctional teams.

Nice to have:

Bachelors degree in computer science Data Science or a related field.

Azure ML Studio Data bricks Fabric

Familiarity with agile software development lifecycle (SCRUM Kanban etc.)

Remote Work :

No