Lead Machine Learning Engineer (MLOps, KServe + building Kubernetes Clusters, PyTorch, TensorFlow on AWS)
Tasks
- Apply continuous integration and continuous deployment best practices
- Automate tests and deployment for machine learning applications
- Build and optimize data pipelines for machine learning
- Collaborate with product and data science teams in Agile delivery
- Design build and deliver machine learning models
- Develop and review model and application code
- Ensure availability performance and security for machine learning applications
- Follow responsible and explainable AI best practices
- Leverage cloud architectures to deploy machine learning at scale
- Retrain, maintain, and monitor models in production
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Automated testing | Bias Variance | Continuous Deployment | Continuous integration | Dask | Data Pipelines | Dimensionality | Dimensionality Reduction | Distributed Computing | Feature Selection | Hyperparameter Tuning | Java | KServe | Kubernetes | MLOps | Machine Learning | Model Training | Model Validation | Model retraining | Monitoring | PyTorch | Python | Scala | Scikit-learn | Spark | TensorFlow | Test automation
Education
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