GPU-Accelerated Video Analytics Engineer – Real-Time Safety

Ginas Tech Jobs; Town of Charlotte (NY); On-site; USD 120,000 - 165,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Savings plan

Job summary

Ginas Tech Jobs is seeking an AI Engineer for GPU‑accelerated video analytics in Charlotte, NC. This onsite role focuses on real‑time safety monitoring across large fleets, processing high‑volume video streams with YOLO‑based detection and temporal smoothing.

You will optimize inference pipelines, integrate with Azure services, and collaborate on dataset curation, model training, and production deployments using Docker and cloud tooling.

Qualifications

• 3+ years of experience shipping computer vision or ML systems to production.
• Strong proficiency in Python and experience with OpenCV, PyTorch, async I/O, and API integrations.
• Hands-on experience with YOLO/Ultralytics or similar object detection frameworks.
• Solid understanding of video processing fundamentals: frame sampling, temporal filtering, confidence thresholds, and multi-camera aggregation.
• Experience optimizing GPU inference performance: batching, stride, TensorRT, CUDA, quantization, throughput tuning.
• Experience with Azure Event Hub, Blob Storage, Application Insights, or similar cloud messaging/storage is a plus.
• Familiarity with Docker, cloud deployments, and production monitoring systems is a plus.
• Experience in temporal/sequence analysis for event detection is a plus.
• Background in video analytics for safety, compliance, or industrial/transportation environments is a plus.

Responsibilities

• Develop and optimize GPU-accelerated video inference pipelines, including batching, stride control, and throughput tuning.
• Implement, evaluate, and improve object detection models (YOLO or similar) and build temporal smoothing/tracking logic for safety event detection.
• Optimize model performance using TensorRT, ONNX, CUDA, and GPU profiling tools to maximize throughput and minimize latency/VRAM usage.
• Build and maintain integrations with event-driven APIs, Azure Event Hub, Blob Storage, and internal services.
• Add robust metrics, logging, telemetry, and fail-safe mechanisms for resilient inference jobs.
• Collaborate on dataset curation, labeling, model training, validation, and experiment tracking.
• Support containerized deployments (Docker) and assist with monitoring and scaling production workloads.

Skills

Python

OpenCV

PyTorch

YOLO

Async I/O

API integrations

Docker

CUDA

TensorRT

ONNX

Tools

Docker

CUDA

TensorRT

ONNX

Azure Event Hub

REST APIs

Job description

Ginas Tech Jobs is seeking an AI Engineer for GPU‑accelerated video analytics in Charlotte, NC. This onsite role focuses on real‑time safety monitoring across large fleets, processing high‑volume video streams with YOLO‑based detection and temporal smoothing.

You will optimize inference pipelines, integrate with Azure services, and collaborate on dataset curation, model training, and production deployments using Docker and cloud tooling.