Machine Learning Engineer

Description :

Federated Learning Based Ctrl Sys on Edge

We are seeking a skilled Machine Learning Engineer to develop and deploy federated learning models on edge devices

Machine Learning Engineer - Federated Learning on Edge Devices

Job Summary :

We are seeking a skilled Machine Learning Engineer to develop and deploy federated learning models on edge devices. You will design privacy-preserving, distributed ML systems that operate efficiently in resource-constrained environments. This role involves close collaboration with cross-functional teams to optimize model performance, ensure data security, and enable real-time inference at the edge.

Top 3 Skills :

- Federated Learning Frameworks - Proficiency with tools like TensorFlow Federated, PySyft, or Flower.

- Edge Deployment - Experience optimizing and deploying ML models on edge platforms (e.g., NVIDIA Jetson, Raspberry Pi, Android).

- Privacy & Security - Strong understanding of data privacy, secure aggregation, and encryption techniques in distributed systems.

Key Responsibilities :

- Design and implement federated learning algorithms tailored for edge computing environments.

- Optimize ML models for low-latency, low-power edge devices.

- Develop secure communication protocols for decentralized training.

- Monitor and evaluate model performance across distributed nodes.

- Collaborate with hardware, software, and data teams to integrate solutions end-to-end.