Senior AI/ML Platform Engineer
Tasks
- Architect and scale ML platform
- Build experiment tracking and model registry
- Create model ready data pipelines
- Define ML governance reproducibility and CI CD best practices for ML
- Design secure scalable ML lifecycle infrastructure
- Ensure security privacy and regulatory compliance for ML
- Implement model training and hyperparameter tuning infrastructure
- Lead technical discussions and mentor engineers
- Optimize ML workload performance across compute and storage
- Orchestrate ML workflows
Perks/Benefits
- Company sponsored retirement plan
- Continual development
- Flexible work environment
- Health and wellness benefits
- Internal career growth opportunities
- Paid time off
- Volunteer time off
Skills/Tech-stack
AWS | Airflow | Amazon SageMaker | Apache Spark | Argo Workflows | CI/CD | Containerization | Data Privacy | Databricks | Deep learning | Distributed Systems | Docker | Drift Detection | Experiment tracking | Feast | Feature Store | Flink | Go | Google Cloud | Hyperparameter Tuning | Java | Kafka | Kubeflow | Kubernetes | Logistic Regression | MLOps | MLflow | Machine Learning | Microservices | Microsoft Azure | Model Drift | Model Drift Detection | Model Governance | Model Interpretability | Model Monitoring | Model Registry | Model auditability | Neural Networks | Python | Random Forest | Random Forests | Reproducibility | Spark Structured Streaming | Statistical Learning | Structured Streaming | TeamCity | Tecton | Terraform | Transformers | Vertex AI | XGBoost
Education
Regions
Countries
States
Cities
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