Advanced Embedded Engr

1. 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

2. 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.

3. 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