MLOps Engineer
7 days ago
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 :
years of professional experience in MLOps, DevOps, or a similar role.
- Strong proficiency in Python and experience with machine learning frameworks like TensorFlow or PyTorch.- Hands-on experience with cloud platforms (AWS, Azure, or GCP) and their respective ML services.
- Proven experience with containerization (Docker) and orchestration (Kubernetes).
- Familiarity with CI/CD tools and version control systems (Git).
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
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