Deep Learning Engineer
Tasks
- Build and maintain reproducible MLOps pipelines with experiment tracking and dataset versioning
- Collaborate with cross functional teams to deliver production ready AI solutions
- Define evaluation benchmarks and ensure model quality through automated testing
- Design data collection labeling and annotation pipelines
- Develop synthetic datasets using Blender and Python based rendering pipelines
- Optimize models for deployment on embedded hardware
- Own deep learning model lifecycle from data collection to deployment
- Train evaluate and optimize detection segmentation keypoint and tracking models
Perks/Benefits
- N/A
Skills/Tech-stack
Blender | Computer Vision | DVC | Dataset versioning | Detection | Docker | Embedded Systems | Experiment tracking | Git | Image Processing | Keypoint detection | Kubeflow | Linux | MLOps | MLflow | ONNX Runtime | Object Tracking | Pose Estimation | PyTorch | Python | Segmentation | TensorFlow | TensorFlow Lite | TensorRT | Weights and Biases
Education
Roles
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