MLOps / Machine Learning Engineer
Tasks
- Build distributed evaluation pipelines
- Build distributed training pipelines
- Build monitoring dashboards
- Deploy trained models
- Design scalable ML infrastructure
- Ensure reproducible ML workflows
- Identify performance bottlenecks across the ML stack
- Implement data pipelines and checkpointing
- Improve cloud GPU cost efficiency
- Manage models and experiments
- Monitor compute usage and model performance
- Optimize model inference performance
- Optimize model training performance
- Reduce ML iteration time
- Track experiments and evaluation metrics
Perks/Benefits
Skills/Tech-stack
Checkpointing | Cloud Computing | Data Pipelines | Deep reinforcement learning | Distributed Systems | Distributed Training | Experiment tracking | GPU Utilization | Inference Optimization | Machine Learning | Machine Learning Pipelines | Model Evaluation | Model Monitoring | Performance Tuning | PyTorch | Python | Reinforcement Learning
Education
N/A
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