MLOps Engineer - CI/CD Pipeline

Key Responsibilities :

- Design and implement end-to-end MLOps pipelines for continuous integration, continuous delivery, and continuous training (CI/CD/CT) of ML models.

- Manage and optimize the production environment for ML models, using containerization (Docker) and orchestration (Kubernetes) to ensure scalability and reliability.

- Implement monitoring and logging solutions to track model performance, data drift, and potential anomalies in production.

- Collaborate with data scientists to transition models from research and development into a production-ready state.

- Automate the retraining and redeployment of models to maintain accuracy and adapt to new data.

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

- Stay updated on the latest MLOps tools, technologies, and best practices.

Required Qualifications :

- 3+ years of professional experience in MLOps, DevOps, or a similar role.

Preferred Qualifications :

- Experience with specific MLOps platforms like Kubeflow, MLflow, or SageMaker.

- Knowledge of data engineering and building data pipelines.

- A strong understanding of machine learning concepts and algorithms