Engineering AI systems
that operate at scale.

The platform operates across large-scale distributed deployments, creating a distributed environment where video, alarms, telemetry, and operational events have to move reliably between remote infrastructure and centralized services. Developed and integrated computer-vision inference, real-time event processing, containerized services, cloud infrastructure, and hardware communication components for production deployments.

Security events need to move from remote systems into processing and operator-facing services with minimal delay. I worked on the event-processing and communication layers using Apache Kafka, WebSockets, and REST APIs, connecting incoming security events with downstream services and real-time interfaces.

My primary focus included the computer-vision layer. I trained and deployed YOLO and PyTorch-based vision models for security-event detection and worked on the inference pipeline that processes live RTSP video and converts visual observations into structured events. The challenge was making inference reliable enough to operate continuously within a production video pipeline.

Production surveillance also requires integrating physical security hardware with software systems. Integrated legacy security hardware with modern backend services using industry-standard communication protocols.

The system is composed of containerized services deployed across multiple environments. I worked with Docker, GitHub, and CI/CD workflows to automate deployment across production infrastructure (AWS, Azure, and OCI). Beyond the cloud, I also manage the physical networking and remote administration of GPU-heavy industrial servers required for intensive edge inference.