MLOps Engineer
3 days ago
Job Summary:
As an MLOps Engineer, you will be responsible for designing, building, and maintaining robust, scalable, and secure machine learning infrastructure. You'll collaborate with data scientists, software engineers, and DevOps to automate and optimize the entire machine learning lifecyclefrom data ingestion and model training to deployment, monitoring, and retraining.
Key Responsibilities:
- Design and implement CI/CD pipelines for machine learning workflows.
- Automate the deployment of machine learning models into production environments.
- Develop infrastructure and tooling for model versioning, tracking, monitoring, and rollback (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).
- Work closely with data scientists to productionize notebooks and scripts.
- Ensure reproducibility, scalability, and security of ML systems.
- Manage cloud infrastructure (e.g., AWS, GCP, Azure) and container orchestration platforms like Kubernetes or Docker Swarm.
- Monitor deployed models for performance degradation, data drift, and errors.
Preferred Qualifications:
- Experience working with model registries and feature stores.
- Knowledge of data versioning and data validation practices.
- Familiarity with deep learning frameworks (TensorFlow, PyTorch) and ML libraries (scikit-learn, XGBoost).
- Understanding of software development practices, testing, and version control (Git).
- Knowledge of security and compliance best practices in ML systems.
- Previous experience working in agile/scrum teams.
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