Senior Computer Vision Specialist

1 month ago

BangaloreDelhi, India Gloify Full time
Job Overview

Gloify is seeking a highly skilled and experienced Computer Vision Engineer to join our innovative team. The ideal candidate will possess hands-on experience in developing and deploying computer vision models and applications.

Key Responsibilities:
  • Algorithm Development: Design, develop, and implement computer vision algorithms for tasks such as object detection, image classification, facial recognition, and video analysis.
  • Model Optimization: Develop and optimize deep learning models for image and video processing using frameworks like TensorFlow, PyTorch, or OpenCV.
  • Dataset Preprocessing: Preprocess large-scale datasets including images and videos to build high-performance computer vision models.
  • Model Deployment: Deploy computer vision models into production, ensuring scalability, performance, and reliability.
  • Collaboration: Collaborate with cross-functional teams to integrate computer vision solutions with existing systems.
  • Research: Stay up to date with the latest research and advancements in computer vision and related fields.
  • Testing and Validation: Perform testing and validation of computer vision models to ensure high accuracy and robustness.
  • Debugging: Debug and troubleshoot issues in the model's performance or system integration.
Required Skills and Qualifications:
  • Experience: 4-5 years of experience in computer vision, image processing, and deep learning.
  • Programming Languages: Strong proficiency in programming languages like Python and experience with computer vision libraries such as OpenCV or PIL.
  • Deep Learning Frameworks: Hands-on experience with deep learning frameworks like TensorFlow, Keras, or PyTorch.
  • Image Processing: Solid understanding of image processing techniques (e.g., filtering, feature extraction, edge detection).
  • Algorithms: Experience with algorithms for object detection, segmentation, image classification, or video tracking.
  • Machine Learning Pipelines: Familiarity with machine learning pipelines and tools (e.g., Scikit-learn, Pandas).
  • Cloud Platforms: Knowledge of cloud platforms like AWS, Azure, or GCP for deploying models.
  • Version Control: Proficient with version control systems (Git) and collaborative development workflows.

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