Edge AI Engineer
Tasks
- Apply model compression techniques
- Build cross platform edge inference runtimes
- Build privacy preserving telemetry pipelines
- Create technical documentation and runbooks
- Design edge AI solutions for mobile and embedded hardware
- Design hybrid edge cloud architectures
- Develop benchmarking suites
- Evaluate bias and privacy considerations
- Implement on device model updates and rollback workflows
- Implement secure execution and model integrity verification
- Optimize models for accelerator backends
- Quantize and optimize models for edge constraints
- Tune latency energy efficiency and memory footprint
Perks/Benefits
Skills/Tech-stack
Benchmarking | Bias Evaluation | C++ | Core ML | Edge inference | Energy Efficiency | Federated Learning | Hybrid Cloud | Inference Optimization | Integrity verification | Knowledge Distillation | Latency optimization | Machine Learning | Model Compression | Model versioning | ONNX | ONNX Runtime | Performance Profiling | Privacy | Pruning | Python | Quantization | Secure execution | Telemetry | TensorFlow | TensorFlow Lite
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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