Machine Learning Operations Engineer (w/m/d)
Tasks
- Build and operate model serving platform
- Containerize and deploy models
- Design input and output schemas for ML models
- Develop inference APIs for batch and streaming
- Integrate model frameworks and types
- Optimize inference performance latency throughput GPU utilization
Perks/Benefits
- Additional location-based benefits
- Company pension scheme
- Conference participation
- Flexible working hours
- Home office
- Job rotation bike program
- Sabbaticals
- Training
- Workation
Skills/Tech-stack
Batching | CUDA | Docker | FP16 | FP32 | GPU Computing | GPU Utilization | Inference | Inference API | Inference Server | Input Output | Input output schemas | Latency optimization | Machine Learning | Model Serving | Performance optimization | PyTorch | Python | Quantization | Streaming | TensorFlow | Throughput Optimization | Triton Inference | Triton Inference Server
Education
N/A
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