Senior Computer Vision Engineer (Video Analytics & Command Control)
Summary
Designs and scales high-throughput computer vision pipelines (Python/C++ with NVIDIA DeepStream, GStreamer, OpenCV) that turn hundreds of live CCTV feeds into real-time alerts streamed via Kafka/MQTT/gRPC into Command and Control systems, with sub-second latency using TensorRT/ONNX inference optimization.
Overview of role:
The Senior Computer Vision Engineer (Video Analytics & Command Control) designs, scales, and deploys high-throughput vision pipelines that feed intelligence directly into centralized Command and Control (C2) systems.
This role bridges edge video processing with enterprise situational awareness layers. The engineer converts raw feeds from massive CCTV networks into real-time, actionable alerts, enabling operators in integrated operations centers to make critical, time-sensitive decisions based on automated visual insights.
Responsibilities:
- Architect robust Python/C++ pipelines using frameworks like NVIDIA DeepStream, GStreamer, or OpenCV to execute concurrent video analytics across hundreds of live camera feeds.
- Translate computer vision model outputs (bounding boxes, track IDs) into lightweight, structured JSON/Protobuf metadata payloads. Stream these alerts instantly to C2 event brokers using Kafka, MQTT, or gRPC.
- Develop algorithms for cross-camera re-identification (Re-ID) and spatial-temporal tracking, allowing the C2 system to map a subject’s trajectory across a physical site or facility layout.
- Write custom plugins and middleware to interface directly with enterprise-grade Video Management Systems, network video recorders (NVRs), and unified access control physical layers.
- Profile, benchmark, and scale inference architectures using tools like TensorRT or ONNX Runtime to maintain strict sub-second end-to-end latency boundaries from camera sensor to C2 dashboard.
- Virtual/Physical tripwire sensors, laser barriers, and IoT occupancy sensors.
- Dry contact relays, Wiegand-to-IP converters, OSDP (Open Supervised Device Protocol), and Programmable Logic Controllers (PLCs).
Requirements:
- 5+ years of software engineering experience, with 3+ years specialized in deploying large-scale video analytics or telemetry pipelines integrated with operational monitoring systems (C2, VMS, or Smart City platforms).
- Elite proficiency in Python and performance-critical C++.
- Deep expertise in PyTorch/TensorFlow, NVIDIA DeepStream SDK, GStreamer, Docker, and distributed message queues (Kafka/RabbitMQ).
- Thorough understanding of real-time streaming protocols (RTSP, WebRTC, SRT, HLS) and enterprise security transport configurations.