Embedded Engr II

Qualifications

• Software Engineering
• Strong experience in Linux application development on both embedded and server platforms Proficiency in modern C++ (14/17/20) Solid Python experience for scripting, tooling, and service development Knowledge of distributed systems and message‐bus architectures (e.g., MQTT, Kafka) Hands-on experience with REST and gRPC API design and implementation. Familiarity with CI/CD workflows and tools such as GitHub Actions, CMake, Docker, and Kubernetes Strong unit testing skills using frameworks such as GTest and pytest
• Video Analytics & Embedded AI
• Experience with GStreamer and custom plugin development for cross-platform. Understanding of inference runtimes such as ONNX Runtime, TensorRT, or OpenVINO Exposure to CV/AI workloads on edge hardware (NPU, GPU, DLA) Familiarity with RTSP and shared‐memory buffer integrations Understanding of computer vision algorithms such as object detection, object tracking, and segmentation etc.
• MLOps
• Experience with MLOps tools for model monitoring, issue detection, and retraining workflows Hands-on with MLflow for experiment tracking and model registry Knowledge of dataset versioning tools like DVC or equivalents Familiarity with observability tools such as Evidently, Grafana, and Prometheus
• ==Nice to Have==
• Experience with video analytics development. Experience developing custom Node‐RED nodes or packaging flows. Background in physical security, VMS/NVR systems, or surveillance analytics Hands-on experience with Ambarella SoCs (CV25 / CV28 / CV72) and EazyAI / CVflow toolchains Understanding of advanced CV algorithms (e.g., pose estimation, re-identification algorithms) Experience with Label Studio for annotation project setup and model‐assisted labeling Integration experience with LLM/VLM models (e.g., LLaVA, Qwen‐VL) in real‐time data pipelines or agent-based systems