About The Position
Dev.Pro is seeking a Computer Vision Engineer to build GPU-accelerated video analytics for real-time safety monitoring across large fleets and industrial environments. The role focuses on designing and optimizing pipelines that detect and track actionable safety events in high-volume video streams. You will develop robust backend solutions that process video data and provide actionable insights using pre-trained computer vision models, working with engineering and product teams to deliver reliable, high-performance systems.
Requirements
- 7+ years of software engineering experience with strong backend skills.
- 3+ years of experience shipping computer vision solutions to production.
- Strong Python proficiency.
- Experience with OpenCV, PyTorch, async I/O frameworks, and API integrations.
- Hands-on experience with YOLO/Ultralytics or similar object detection frameworks.
- Experience detecting, labeling, and tracking objects in video.
- Solid knowledge of frame sampling, confidence thresholds, temporal filtering, and multi-camera aggregation.
- Experience with GPU inference optimization, including batching, stride, TensorRT, CUDA, quantization, and throughput tuning.
- Ability to communicate and collaborate effectively with engineering and product teams.
- Upper-Intermediate+ English level.
Nice To Haves
- Experience with Azure Event Hub, Blob Storage, Application Insights, or similar.
- Familiarity with Docker, cloud deployments, and production monitoring systems.
- Experience in temporal/sequence analysis for event detection.
- Experience with video analytics in safety, compliance, or industrial/transportation environments.
Responsibilities
- Develop and optimize GPU-accelerated video inference pipelines for real-time processing.
- Implement, evaluate, and improve object detection models such as YOLO or similar.
- Build temporal smoothing and tracking logic for safety event detection.
- Optimize model performance using TensorRT, ONNX, CUDA, and GPU profiling tools.
- Build and maintain integrations with event-driven APIs, Azure Event Hub, Blob Storage, and internal services.
- Add metrics, logging, telemetry, and fail-safe mechanisms for resilient inference jobs.
- Contribute to dataset curation, labeling, and model evaluation; fine-tune or train models as needed.
- Support containerized deployments using Docker and assist with monitoring and scaling production workloads.
Benefits
- Collaborate with top engineers on an innovative, technically challenging computer vision project.
- Build impactful systems that enhance safety across large fleets and industrial operations.
- Gain hands-on experience with GPU-accelerated video analytics, temporal tracking, and real-time safety monitoring.
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What This Job Offers
Career Level
Senior
Education Level
No Education Listed
