MLOps / Machine Learning Engineer
Tasks
- Build distributed training and evaluation pipelines
- Build model management and experiment tracking systems
- Create reproducible ML workflows
- Design scalable ML infrastructure
- Develop dashboards and monitoring tools
- Identify bottlenecks across the ML stack
- Improve cloud cost efficiency
- Improve model accuracy robustness and compute efficiency
- Optimize training and inference performance
- Reduce ML iteration time and improve accuracy
Perks/Benefits
Skills/Tech-stack
Cloud Computing | Data Pipelines | Deep learning | Deep reinforcement learning | Distributed Training | Experiment tracking | GPU Utilization | Inference Optimization | Machine Learning | Model Evaluation | PyTorch | Python | Reinforcement Learning | Version control
Education
N/A
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