Overview
On Site
Hybrid
BASED ON EXPERIENCE
Full Time
Skills
Extraction
Real-time
Collaboration
Computer Science
Electrical Engineering
Artificial Intelligence
Machine Learning (ML)
Image Processing
Deep Learning
.NET
OpenCV
PyTorch
Training
Python
Computer Vision
Step Detection
SSD
Semantics
Software Asset Management
Video
Algorithms
3D Computer Graphics
Estimating
Optimization
GPU
CUDA
SANS
Cloud Computing
Machine Learning Operations (ML Ops)
Job Details
Role Overview
Develop and deploy computer vision solutions using deep learning, image processing, and modern CV frameworks. Build production-ready systems for visual understanding and analysis.
Responsibilities
- Design and implement computer vision models for object detection, segmentation, and classification
- Develop image processing pipelines for enhancement, filtering, and feature extraction
- Build and optimize CNN architectures for various computer vision tasks
- Implement CV solutions using OpenCV for real-time image and video processing
- Train and fine-tune deep learning models using PyTorch for production deployment
- Optimize models for performance, accuracy, and inference speed
- Collaborate with cross-functional teams to integrate CV solutions into products
Requirements
- Bachelor's degree in Computer Science, Electrical Engineering, AI/ML, or related field
- Strong expertise in image processing techniques and algorithms
- Proven experience with CNNs and deep learning architectures (ResNet, YOLO, U-Net, etc.)
- Proficiency in OpenCV for computer vision applications
- Hands-on experience with PyTorch for model development and training
- Strong Python programming skills
- Understanding of computer vision fundamentals (feature detection, image transforms, edge detection)
Preferred
- Experience with object detection frameworks (YOLO, Faster R-CNN, SSD)
- Knowledge of semantic/instance segmentation techniques
- Familiarity with vision transformers (ViT, DETR, SAM)
- Experience with video processing and tracking algorithms
- Understanding of 3D vision, depth estimation, or SLAM
- Knowledge of model optimization (TensorRT, ONNX, quantization)
- Experience with GPU programming and CUDA
- Familiarity with cloud deployment and MLOps
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