MLOps Engineer
2 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 :
3+ 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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