Senior Computer Vision Engineer

TOSS-EX PR PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

NEC Asia Pacific Pte Ltd is seeking a Senior Computer Vision Engineer to design, scale, and deploy high-throughput vision pipelines that feed centralized Command and Control (C2) systems. This role bridges edge video processing with enterprise situational awareness layers, turning CCTV feeds into real-time alerts for operators in integrated operations centers.

You will work across Python and C++, leveraging NVIDIA DeepStream, TensorRT, and OpenCV to meet sub-second latency, while integrating

Qualifications

• 5+ years of software engineering experience, with 3+ years deploying large-scale video analytics or telemetry pipelines.
• Proficiency in Python and performance-critical C++.
• Experience with PyTorch/TensorFlow and NVIDIA DeepStream SDK.
• Familiarity with distributed messaging systems (Kafka/RabbitMQ).
• Knowledge of real-time streaming protocols (RTSP/WebRTC/SRT/HLS) and secure transport configurations.

Responsibilities

• Architect Python/C++ pipelines for hundreds of live camera feeds.
• Translate model outputs into lightweight JSON/Protobuf payloads and stream via Kafka/MQTT/gRPC.
• Develop multi-camera tracking and cross-camera re-identification for enterprise sites.
• Create plugins to interface with VMS, NVRs, and access control systems.
• Profile and scale inference architectures to maintain sub-second latency from camera to C2 dashboard.
• Integrate hardware interfaces and edge-to-enterprise data pathways.

Skills

Python

C++

Video analytics

Deep learning

Streaming

Tools

NVIDIA DeepStream

GStreamer

OpenCV

Docker

Kafka

RabbitMQ

gRPC

TensorRT

ONNX Runtime

Job description

The Senior Computer Vision Engineer (Video Analytics & Command Control) at NEC Asia Pacific Pte Ltd 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:

• C2-Scale Analytics Pipelines: Architect robust Python/C++ pipelines using frameworks like NVIDIA DeepStream, GStreamer, or OpenCV to execute concurrent video analytics across hundreds of live camera feeds.
• Event Telemetry & Alert Generation: 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.
• Multi-Camera Tracking & Fusion: Develop algorithms for cross-camera re-identification (ReID) and spatial-temporal tracking, allowing the C2 system to map a subject’s trajectory across a physical site or facility layout.
• VMS & Enterprise Integration: Write custom plugins and middleware to interface directly with enterprise-grade Video Management Systems, network video recorders (NVRs), and unified access control physical layers.
• Performance Tuning for Command Centers: Profile, benchmark, and scale inference architectures using tools like TensorRT or ONNX Runtime to maintain strict sub-second end-toend latency boundaries from camera sensor to C2 dashboard.
• Perimeter Security Sensors: Virtual/physical tripwire sensors, laser barriers, and IoT occupancy sensors.
• Hardware Integration Layers: Dry contact relays, Wiegand-to-IP converters, OSDP (Open Supervised Device Protocol), and Programmable Logic Controllers (PLCs).

Requirements:

• Experience: 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).
• Programming Languages: Elite proficiency in Python and performance-critical C++.
• Frameworks & Infrastructure: Deep expertise in PyTorch/TensorFlow, NVIDIA DeepStream SDK, GStreamer, Docker, and distributed message queues (Kafka/RabbitMQ).
• Networking & Protocols: Thorough understanding of real-time streaming protocols (RTSP, WebRTC, SRT, HLS) and enterprise security transport configurations.