Computer Vision Developer

Role Overview

The Computer Vision Engineer designs, builds, and deploys machine learning and deep learning solutions that enable systems to analyze and interpret visual data. Reporting to the Application Developer Associate Manager, this role builds production-ready computer vision algorithms for tasks like object recognition, image analysis, and video processing, integrating them into broader enterprise software applications.

Key Responsibilities

• Algorithm Development: Research, design, and implement computer vision algorithms for image segmentation, object detection, and visual tracking.
• Model Training & Fine-Tuning: Train and optimize deep learning models on large visual datasets to elevate performance, precision, and processing efficiency.
• Data Curation & Annotation: Collect, clean, and pre-process image and video streams, creating accurate annotation datasets for robust model training.
• System Integration & Deployment: Integrate computer vision pipelines into core applications, ensuring end-to-end stability, scalability, and security.
• Tool & Library Assessment: Evaluate emerging development tools, third-party libraries, and CV frameworks to accelerate engineering workflows and maintain high code quality.

Qualifications & Requirements

• Education: Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, Data Science, or a related field.
• Experience: 3 to 5 years of hands-on software development experience, with direct focus on application development and computer vision projects.
• Core Technical Stack: Proficiency in Python, OpenCV, PyTorch, TensorFlow, CUDA, or CVAT for developing computer vision models and pipelines.
• Engineering Best Practices: Practical knowledge of unit/integration testing, version control tools (Git), API integration, and debugging workflows.

Key Competencies

• Strong problem-solving skills to optimize models for execution speed, memory footprint, and accuracy.
• Ability to collaborate effectively across functional business units, translating complex technical limits into non-technical terms.
• High attention to detail regarding secure coding standards, data preprocessing, and systematic test coverage.