Computer Vision Intern

Job Summary

The Computer Vision Intern will contribute to the development and optimization of vision-based AI models powering TensorGou2019s enterprise products. This is a hands-on opportunity to work in a fast-paced deep learning environment, solving real-world computer vision challenges and directly impacting model development and product performance. This role will be based in Hyderabad, work from office, for a duration of 6 months as an internship, with a potential PPO based on performance.

Requirements

• Proficiency in Python with the ability to write scripts and handle data processing workflows.
• Basic familiarity with OpenCV, NumPy, TensorFlow, or PyTorch (at least one).
• Understanding of fundamental deep learning concepts (training, validation, loss functions, overfitting).
• Basic knowledge of image data handling and preprocessing.
• Awareness of modern AI concepts such as LLMs or Generative AI.
• Candidate of B.Tech / BCA / BSc / MCA / M Tech / Any equivalent degree (pursuing or completed).
• Good to have - Prior project or coursework or certification specifically involving computer vision tasks.
• Familiarity with model fine-tuning or transfer learning approaches.
• Familiarity with YOLO and detection pipelines.
• Understanding of model evaluation metrics such as precision, recall, and mAP.

Responsibilities

• Develop and test computer vision models using real-world and curated datasets.
• Handle image data preprocessing, annotation, and dataset management.
• Implement computer vision and deep learning techniques under the guidance of senior engineers.
• Evaluate model performance, benchmark results, and document findings clearly.
• Collaborate with the team to understand requirements and iterate on solutions.
• Support the AI engineering team with experimentation, applied research and prototyping.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.