AI & Computer Vision Engineer

Responsibilities:
1. Developing and modeling computer vision models:

● Design, develop and train deep learning models using libraries such as TensorFlow, PyTorch, OpenCV and Keras.

● Implementing and optimizing convolutional neural networks (CNNs) for tasks such as object detection, image segmentation and classification.

2. Data Management and Preparation:

● Perform data preparation, cleaning and annotation to ensure the quality of the sets used in training.

● Working with data annotation tools for image segmentation and labeling.

3. Deploy and monitor models:

● Deploy machine learning models in production using frameworks such as TensorFlow Serving,

4. Docker and Kubernetes.

● Configure and manage deployment pipelines in cloud environments (AWS, Azure, GCP).

● Monitoring the performance of models in production, adjusting and retraining when necessary to ensure efficiency and accuracy.

5. Performance Optimization:

● Optimize models for efficient execution on specific hardware, such as GPUs and CPUs.

● Ensure the scalability and performance of machine learning systems in production.

6. Collaboration and Technical Support:

● Work collaboratively with data, engineering and product teams to align machine learning solutions with business needs.

7. Research and Innovation:

● Keeping up to date with trends and advances in computer vision and deep learning.

● Propose new approaches and solutions to existing problems using supervised, unsupervised and deep learning techniques.

Requirements:

● Minimum of 2 years experience in model development with libraries such as TensorFlow, PyTorch, OpenCV and Keras.

● Experience with modeling and training convolutional neural networks (CNNs) for computer vision tasks.

● Knowledge of object detection, image segmentation and classification algorithms.

● Experience with Machine Learning frameworks and data annotation tools.

● Skill in programming languages such as Python and C++

● Experience with model deployment tools (e.g. TensorFlow Serving, Docker, Kubernetes).