Machine Learning Engineer
Tasks
- Build MLOps infrastructure
- Centralize ML engineering standards
- Create model serving and monitoring systems
- Deploy ML inference services
- Design data and feature management systems
- Develop multi model architectures
- Ensure version control and testing
- Evaluate and manage model overhead and TCO
- Implement CI/CD pipelines
- Implement automated retraining pipelines
- Integrate and optimize computer vision models
- Set up model registry and tracking
- Translate ML prototypes into production code
Perks/Benefits
Skills/Tech-stack
AWS EC2 | AWS EKS | Airflow | Amazon SageMaker | CI/CD | Concept drift | DBT | Datadog | Docker | DynamoDB | FastAPI | Feast | Feature Store | Inference Server | Kubeflow | Kubernetes | MLOps | MLflow | Machine Learning | Metaflow | Model Serving | Monitoring | ONNX | Prometheus | PyTorch | Python | RAG | Ray Serve | Redis | Retrieval-Augmented Generation | S3 | Snowflake | TensorFlow | TensorRT | Terraform | Torchserve | Triton Inference | Triton Inference Server | Vector Database | Visual Retrieval Augmented Generation | Weights and Biases
Education
N/A
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