Sr. Computer Vision & Edge AI Engineer

Overview

• Design, develop, and optimize computer vision models for:
• Object Detection
• Image & Instance Segmentation
• Multi-Object Tracking
• Human Pose Estimation
• Build and train deep learning models using frameworks such as PyTorch or TensorFlow.
• Convert and optimize models for edge deployment using:
• TensorRT
• OpenVINO
• NVIDIA TAO Toolkit
• Perform model quantization, pruning, benchmarking, and latency optimization.
• Deploy models on edge hardware platforms (NVIDIA Jetson, Intel-based edge devices, NVIDIA IGX Orin, etc.).
• Write clean, scalable, and production-ready Python code.
• Collaborate with cross-functional teams including ML engineers, software developers, and hardware teams.
• Conduct performance evaluation, debugging, and continuous improvement of deployed systems.
• Stay updated with the latest research and advancements in computer vision and edge AI.
  
  

• 5+ years of hands-on experience in Computer Vision and Deep Learning.
• Strong expertise in:
• Object detection architectures (e.g., YOLO, Faster R-CNN, SSD)
• Segmentation models (e.g., U-Net, Mask R-CNN)
• Tracking algorithms (e.g., DeepSORT, ByteTrack)
• Pose estimation frameworks (e.g., OpenPose, HRNet)
• Proven experience deploying optimized models using:
• TensorRT
• OpenVINO
• NVIDIA TAO Toolkit
• Strong Python programming skills.
• Experience with model optimization techniques (INT8 quantization, FP16, pruning).
• Familiarity with ONNX and model conversion pipelines.
• Strong understanding of GPU acceleration and edge hardware constraints.