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