Machine Learning & 3D Computer Vision Engineer. 

I am a Machine Learning Engineer specialized in 3D Computer Vision, MLOps, and production-grade AI systems.

My work spans the full lifecycle of intelligent systems: from dataset generation and perception algorithm design to model deployment and continuous learning in cloud environments. I have built and optimized real-time 3D tracking pipelines, implemented object detection and pose estimation systems, and developed GPU-accelerated geometry processing methods—achieving significant runtime improvements in large-scale data pipelines.

My experience includes point cloud reconstruction, sensor fusion, and multi-modal deep learning, combined with strong expertise in PyTorch-based development.

Beyond modeling, I focus on operational excellence: containerization, GCloud deployment, experiment tracking (MLflow), data versioning (DVC), and incremental learning strategies for production systems. I also contribute across the stack when needed, including ROS-based perception systems and web applications (Vue.js, Django).

My approach is pragmatic and systems-oriented: design robust pipelines, optimize computational performance, deploy reliably, and iterate continuously.