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CY iT HR
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#CV #resume #ml #ai #computer_vision
Hi everyone!
I’m Georgy Gunkin - Lead ML/CV Engineer (5+ years) building production, real-time computer vision systems end-to-end: problem framing, data strategy, training, GPU inference optimization, deployment, and production monitoring. Performance-first mindset (NVIDIA Nsight Systems/Compute), reliability-first operations (metrics, monitoring, reproducibility).
Core strengths
• End-to-end ownership of CV/ML systems in production (from data + training to rollout + monitoring)
• Real-time performance engineering: multithreaded/asynchronous pipelines, profiling, GPU-accelerated inference (TensorRT / ONNX Runtime / CUDA)
• Production engineering: Rust / C++ / Python / C#, Docker/Linux; microservices and monoliths
• Leadership: led ML subsystem development and managed a team of 3 ML engineers
Selected production highlights
• Produce inspection conveyor: 2 industrial cameras (4096x3000 @ 23.5 FPS), detection/segmentation + defect pipeline, track-level decisions, stable at full conveyor load
• Retail theft-risk analytics: distributed services (detection/segmentation/pose + multi-camera tracking/ReID + action recognition), 40 1920x1080 streams at 6 FPS with low latency
• NDT for metal products: 4 cameras (2448x2048 @ 79 FPS), 14 product types / 9 defect classes, active-learning data loop + operator GUI
I’m currently exploring Senior/Lead opportunities where I can own the end-to-end lifecycle of CV/ML systems - from problem framing and data strategy to GPU-optimized deployment and production monitoring.
Open to Senior/Lead ML/CV roles (CV Tech Lead / real-time video analytics / inference & performance engineering).
Languages: English (fluent), Russian (native)
Full CV: https://disk.yandex.com/i/JPrRfL33WWvcVg
Contacts
Telegram @ggunkin
[email protected]
Hi everyone!
I’m Georgy Gunkin - Lead ML/CV Engineer (5+ years) building production, real-time computer vision systems end-to-end: problem framing, data strategy, training, GPU inference optimization, deployment, and production monitoring. Performance-first mindset (NVIDIA Nsight Systems/Compute), reliability-first operations (metrics, monitoring, reproducibility).
Core strengths
• End-to-end ownership of CV/ML systems in production (from data + training to rollout + monitoring)
• Real-time performance engineering: multithreaded/asynchronous pipelines, profiling, GPU-accelerated inference (TensorRT / ONNX Runtime / CUDA)
• Production engineering: Rust / C++ / Python / C#, Docker/Linux; microservices and monoliths
• Leadership: led ML subsystem development and managed a team of 3 ML engineers
Selected production highlights
• Produce inspection conveyor: 2 industrial cameras (4096x3000 @ 23.5 FPS), detection/segmentation + defect pipeline, track-level decisions, stable at full conveyor load
• Retail theft-risk analytics: distributed services (detection/segmentation/pose + multi-camera tracking/ReID + action recognition), 40 1920x1080 streams at 6 FPS with low latency
• NDT for metal products: 4 cameras (2448x2048 @ 79 FPS), 14 product types / 9 defect classes, active-learning data loop + operator GUI
I’m currently exploring Senior/Lead opportunities where I can own the end-to-end lifecycle of CV/ML systems - from problem framing and data strategy to GPU-optimized deployment and production monitoring.
Open to Senior/Lead ML/CV roles (CV Tech Lead / real-time video analytics / inference & performance engineering).
Languages: English (fluent), Russian (native)
Full CV: https://disk.yandex.com/i/JPrRfL33WWvcVg
Contacts
Telegram @ggunkin
[email protected]