Machine Learning Operations Engineer
Tasks
- Deploy and operate AWS SageMaker endpoints
- Deploy and operate inference services
- Design production ML pipelines
- Detect model and data drift
- Enable model version promotion rollback recovery
- Establish ML deployment patterns
- Manage high throughput media data movement
- Monitor latency error rates throughput
- Scale inference with autoscaling and load testing
- Select and optimize GPU CPU infrastructure
- Set up A B testing and controlled rollouts
Perks/Benefits
- Company paid life insurance
- Medical, dental & vision coverage
- Mental well-being resources
- Paid parental leave
- Paid sick days
- Paid vacation
- Retirement benefits
Skills/Tech-stack
A/B | A/B Testing | AWS SageMaker | Alerting | Approximate Nearest Neighbor | Autoscaling | Autoscaling Behavior | B testing | BERT | CPU | Computer Vision | Containerization | Drift Detection | Embedding Generation | Entity recognition | GPU | HNSW | Inference Serving | Instance Benchmarking | Kubernetes | Load Testing | Model Evaluation | Monitoring | NLP | Named Entity Recognition | Nearest neighbor | PyTorch | Reranking | Rollback | SageMaker Endpoints | Sagemaker Pipelines | TensorFlow | Transformer | Version control
Education
N/A
Regions
Countries
States
Cities
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