[Hiring] Computer Vision Engineer @Dev.Pro

Role Description

We invite a skilled AI Engineer to join our team in building GPU-accelerated video analytics for real-time safety monitoring across large fleets and industrial environments. In this role, you’ll help design and optimize pipelines that detect and track actionable safety events in high-volume video streams. You’ll work on real-world systems that make an impact, collaborating with engineering and product teams to deliver reliable, high-performance solutions.

Qualifications

• 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 (batching, stride, TensorRT, CUDA, quantization, throughput tuning)
• Ability to communicate and collaborate effectively with engineering and product teams
• Upper-Intermediate+ English level

Requirements

• Experience with Azure Event Hub, Blob Storage, Application Insights, or similar (desirable)
• Familiarity with Docker, cloud deployments, and production monitoring systems (desirable)
• Experience in temporal/sequence analysis for event detection (desirable)
• Experience with video analytics in safety, compliance, or industrial/transportation environments (desirable)

Key Responsibilities

• Develop and maintain robust backend solutions that process video data and deliver actionable insights using pre-trained computer vision models.
• Develop and optimize GPU-accelerated video inference pipelines for real-time processing.
• Implement, evaluate, and improve object detection models (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-tuning/training as needed.
• Support containerized deployments (Docker) and assist with monitoring and scaling production workload.

Next Steps

• Submit a CV in English
• Intro call with a Recruiter
• Interview
• Signing the Contract