AI ENGINEER

1. Algorithm Development:

• Design develop and optimize AI algorithms and models for various applications including computer vision natural language processing and robotics.
• Implement algorithms in C Python MATLAB or other programming languages as needed.

1. Computer Vision and Image Processing:

• Develop computer vision algorithms and techniques for tasks such as object detection recognition tracking and segmentation.
• Utilize OpenCV and other libraries for image processing and analysis.

1. Machine Learning and Deep Learning:

• Build and train machine learning and deep learning models using frameworks such as TensorFlow PyTorch or Keras.
• Apply techniques such as convolutional neural networks (CNNs) recurrent neural networks (RNNs) and reinforcement learning to solve AI problems.

1. Data Processing and Analysis:

• Preprocess and analyze large datasets to extract meaningful insights and features for AI model training.
• Implement data augmentation and augmentation techniques to enhance model performance.

1. Performance Optimization:

• Optimize algorithms and models for speed memory usage and scalability.
• Conduct profiling and benchmarking to identify bottlenecks and areas for improvement.

1. Integration and Deployment:

• Integrate AI solutions into existing systems and applications ensuring compatibility and scalability.
• Deploy models to production environments and monitor performance in realworld settings.

1. Documentation and Reporting:

• Document code algorithms and processes to ensure reproducibility and maintainability.
• Prepare technical reports presentations and documentation for internal and external stakeholders.

1. Bachelors degree in Computer Science Electrical Engineering or related field. Masters or Ph.D. preferred.
2. Proven experience in AI engineering computer vision and machine learning with a focus on algorithm development and implementation.
3. Proficiency in programming languages such as C Python and MATLAB with experience in software development and version control.
4. Strong knowledge of computer vision principles image processing techniques and feature extraction methods.
5. Experience with deep learning frameworks such as TensorFlow PyTorch or Keras and proficiency in implementing neural network architectures.
6. Familiarity with libraries such as OpenCV scikitimage and numpy for image processing and analysis.
7. Excellent problemsolving and analytical skills with the ability to troubleshoot complex issues and propose effective solutions.
8. Strong communication and collaboration skills with the ability to work effectively in a multidisciplinary team environment.

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