Artificial Intelligence (AI) Engineer, Video Analytics, Onsite in Charlotte, NC - M

Artificial Intelligence (AI) Engineer, Video Analytics, Onsite in Charlotte, NC

The Artificial Intelligence (AI) Engineer, Video Analytics will work with a team that builds GPUaccelerated video analytics for realtime safety monitoring across large fleets and industrial environments. The system processes highvolume video streams, runs YOLObased detection models, performs temporal tracking and smoothing to reduce false positives, and identifies actionable safety violations. Inference results are published to downstream APIs and integrated with Azure Event Hub, Blob Storage, and cloud monitoring systems. If you enjoy pushing GPU performance limits, crafting resilient Machine Learning (ML) pipelines, and building realworld safety applications that make an impact, you will fit right in. This position is 100% Onsite and not open for Remote.

Artificial Intelligence (AI) Engineer Responsibilities:

- Develop and optimize GPUaccelerated 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.

Artificial Intelligence (AI) Engineer Qualifications:

- 3+ years of experience shipping computer vision or machine learning systems to production.

- Strong proficiency in Python and experience with OpenCV, PyTorch, async I/O frameworks, 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, model quantization, and throughput tuning.

- Experience with Azure Event Hub, Blob Storage, Application Insights, or similar cloud messaging/storage platforms 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.

- Tech Stack: aiohttp, Application Insights, asyncio, Azure Blob Storage, Azure Event Hub, CUDA, Docker, gRPC, ML - Machine Learning, ONNX, OpenCV, Python, PyTorch, RESTful APIs, Telemetry Tools, TensorRT, and Ultralytics YOLO.

Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc.

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