We are seeking a Computer Vision Engineer to design, develop, and deploy robust computer vision solutions for real-world applications. This is a full-time, on-site role where you will work across the complete development lifecycle—from research and prototyping to model optimization and production deployment—while focusing on performance, accuracy, and scalability.
Responsibilities of the Candidate:
Develop and optimize computer vision pipelines for object detection, segmentation, tracking, and quality assessment.
Implement and experiment with deep learning architectures, including GANs, to improve model performance and data representation.
Build, train, and fine-tune models using Python with PyTorch and/or TensorFlow.
Optimize model inference for edge computing environments and resource-constrained deployments.
Integrate computer vision models into end-to-end software workflows, including data preprocessing, post-processing, and monitoring.
Collaborate with cross-functional teams to translate business requirements into technical solutions.
Maintain clean, well-documented code and support testing and validation for production deployment.
Requirements:
Strong hands-on experience in computer vision model development and evaluation.
Proficiency in Python and deep learning frameworks such as PyTorch and/or TensorFlow.
Experience developing or experimenting with Generative Adversarial Networks (GANs).
Knowledge of edge computing concepts, including model compression, efficient inference, and deployment strategies.
Strong understanding of computer vision algorithms, neural network training workflows, and performance optimization.
Ability to debug machine learning systems, analyze results, and improve model performance through experimentation.