Lead Assistant manager

1. Bachelor's degree in Computer Science or a related field; advanced degree preferred.

2. Minimum 5 years of experience working as an MLOps Engineer or similar role within a data-driven organization.

3. Experience with Kubernetes and KubeFlow is mandatory.

4. Strong understanding of machine learning concepts and algorithms.

5. Proficiency in Python developing ML pipelines/scripts.

6. Experience with popular MLOps toolkits such as Kubeflow Pipelines, TensorFlow Extended (TFX), MLflow, etc., is essential

7. Solid knowledge of containerization technologies like Docker and Kubernetes for deploying ML models at scale.

8. Familiarity with cloud platforms like AWS/Azure/GCP for building scalable infrastructure solutions is highly desirable

9. Experience with version control systems like Git/GitHub for managing code repositories

10. Excellent problem-solving skills with the ability to analyze complex technical issues related to ML model deployments.

1. Develop and maintain end-to-end Data Engineering pipelines for deploying, monitoring, and scaling machine learning models.

2. Collaborate with data scientists, software engineers, and DevOps teams to ensure seamless integration of ML models into production systems.

3. Optimize model deployment processes by leveraging containerization technologies such as Docker or Kubernetes.

4. Implement continuous integration/continuous deployment (CI/CD) practices for ML model development lifecycle management.

6. Monitor deployed ML models in production environments to identify performance issues or anomalies.

7. Work closely with cross-functional teams to troubleshoot issues related to model performance or data quality in production systems.

8. Stay up-to-date with the latest advancements in MLOps toolkits, frameworks, best practices, and industry trends.

1. Bachelor's degree in Computer Science or a related field; advanced degree preferred.