AI Platform & Machine Learning Engineer
Tasks
- Build model registry capabilities
- Build reproducible training environments
- Build scalable AI service APIs
- Contribute to technical architecture decisions
- Create dataset ingestion workflows
- Define engineering standards and automation
- Deploy machine learning models to cloud environments
- Deploy models to embedded NVIDIA platforms
- Design CI CD pipelines for machine learning
- Design scalable machine learning infrastructure
- Develop annotation and labeling workflows
- Develop automated benchmarking and model validation pipelines
- Develop continual learning pipelines
- Develop distributed training pipelines
- Generate synthetic data
- Implement experiment tracking
- Implement model versioning
- Manage and validate training datasets
- Monitor deployed model performance
- Optimize inference infrastructure
- Support PEFT and LoRA techniques
- Support automated model retraining
- Support federated learning
- Train evaluate and deploy computer vision models
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Airflow | Annotation tools | Azure | BIM | CI/CD | CUDA | CUDA toolkit | Computer Vision | Data labeling | Dataset Management | Distributed Training | Docker | Experiment tracking | Federated Learning | Google Cloud | Kubeflow | Kubernetes | LoRA | MLflow | Machine Learning | Model Registry | Model versioning | NVIDIA Jetson | ONNX | PEFT | PyTorch | Python | Ray | Semantic Reasoning | TensorFlow | TensorRT | Weights & Biases
Education
N/A
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