ML Operations Engineer

• Design, implement, and manage the infrastructure required for deploying machine learning models into production.
• Ensure seamless integration of ML models with existing systems and applications.
• Develop and implement monitoring solutions to track the performance and accuracy of deployed models.
• Troubleshoot and resolve issues related to model performance and data quality.
• Regularly update and retrain models to maintain their accuracy and effectiveness.
• Implement continuous integration and continuous deployment (CI/CD) pipelines for ML models to streamline the deployment process.
• Automate workflows and processes to improve the efficiency of model deployment and management.
• Work closely with data scientists, data engineers, and software engineers to ensure smooth model deployment and operation.
• Communicate technical concepts and solutions effectively to both technical and non-technical stakeholders.
• Ensure that deployed models comply with relevant security and privacy regulations.
• Implement best practices for data security and model governance.

Let s talk about your skills/expertise:

• Bachelor s or Master s degree in Computer Science, Data Science, Engineering, or a related field.
• Proven experience (5+ years) in ML Ops, DevOps, or a related field, with a focus on deploying and managing machine learning models.
• Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Proficiency with cloud platforms (e.g., AWS, Azure, Google Cloud) and their machine learning services.
• Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
• Strong understanding of CI/CD principles and tools (e.g., Jenkins, GitLab CI).