As we understand it, AI and ML are critical to the delivery of services for any enterprise, which requires a combination of technical skills, knowledge of AI and ML algorithms and libraries, and an understanding of data science and computer vision techniques. Additionally, it is important to have a good understanding of the ethical and fairness considerations related to AI development.

• ML Engineer
• AI Engineer
• Research Scientist

We have working knowledge in the following areas:

1. Programming languages: knowledge of programming languages such as Python, R, and Java, which are commonly used for AI and ML development.
2. Machine learning libraries: familiarity with popular machine learning libraries such as TensorFlow, PyTorch, and Scikit-Learn
3. Data Science: Understanding of data science concepts, including data pre-processing, data visualization, and statistical analysis.
4. Algorithms: Knowledge of common AI and ML algorithms, such as supervised and unsupervised learning, deep learning, and natural language processing (NLP).
5. Deep learning frameworks: familiarity with popular deep learning frameworks such as Keras, PyTorch, and TensorFlow.
6. Neural Networks: Understanding of neural networks, including feed-forward, convolutional, and recurrent neural networks.
7. Computer Vision: Knowledge of computer vision techniques, including image classification, object detection, and image segmentation.
8. Natural Language Processing: Understanding of natural language processing techniques, such as text classification, sentiment analysis, and language translation.
9. Model Deployment: Understanding how to deploy ML models in production, including model optimization, performance monitoring, and scaling.
10. Ethics and Fairness: Knowledge of ethical and fairness considerations, such as bias and explainability, is essential when developing and deploying AI models.