- Design and develop Linux-based applications and services for edge devices.

- Work on kernel-level and user-space development.

- Develop, optimize, and deploy AI/ML models on edge hardware platforms.

- Integrate AI pipelines with embedded Linux systems.

- Optimize system performance, memory management, and power consumption.

- Work on device drivers and board support packages (BSP).

- Implement containerization (Docker/Podman) for edge deployments.

- Collaborate with AI/ML engineers to convert trained models into optimized edge inference solutions.

- Debug system-level issues and optimize boot time, latency, and throughput.

- Work closely with hardware teams for SoC-level integration.

Technical Skills :

Linux Expertise :

- Strong experience in Linux internals, kernel, and system programming.

- Hands-on experience in C/C++ development.

- Multi-threading, IPC, memory management.

- Experience with Buildroot / Yocto.

- Experience in cross-compilation and embedded toolchains.

- Device driver development (preferred).

Edge AI / AI Deployment :

- Experience deploying AI/ML models on edge devices.

- Knowledge of TensorFlow Lite, ONNX Runtime, OpenVINO, or similar frameworks.

- Model optimization techniques (quantization, pruning).

- Experience with NVIDIA Jetson / Qualcomm / ARM-based platforms.

- Understanding of GPU, NPU acceleration.

- Real-time inference optimization.

Additional Skills (Good to Have) :

- GStreamer / Video processing pipelines.

- Computer Vision exposure.

- Docker & Kubernetes (Edge deployments).

- CI/CD for embedded systems.

- Knowledge of networking protocols (MQTT, TCP/IP).