Job
Description
Develop and implement machine learning models using Python-based frameworks. Collaborate with the AI team to build, train, and optimize models for various applications. Utilize AI libraries such as TensorFlow, PyTorch, and spaCy to develop solutions. Contribute to the design and development of AI systems that leverage large language models (LLMs) and other advanced AI techniques. Work with data preprocessing, data augmentation, and model evaluation techniques. Assist in AI model deployment and integration with applications. Required AI Skills and Knowledge: Basic understanding of AI concepts such as supervised and unsupervised learning, classification, clustering, and neural networks. Knowledge of AI libraries and frameworks TensorFlow, PyTorch, or Keras for deep learning. spaCy for natural language processing. scikit-learn for traditional machine learning. Familiarity with Large Language Models (LLMs) , such as GPT, and their applications in natural language understanding and generation. Understanding of data preprocessing techniques , feature engineering, and model evaluation metrics. Awareness of AI ethics and responsible AI development practices. Preferred Skills (Not Mandatory): Exposure to cloud-based AI development platforms (e.g., Google Cloud AI, AWS AI, or Azure AI). Familiarity with optimization techniques for AI model performance. Understanding of deep learning architectures like CNNs, RNNs, and transformers. Required Programming Skills: Python proficiency is essential. Familiarity with Python libraries like NumPy, pandas, and matplotlib for data manipulation and visualization. Experience with version control systems like Git. Training and Development Opportunities: In-depth training on AI development, including machine learning and natural language processing. Exposure to Large Language Models (LLMs) and their use cases. Hands-on experience with AI frameworks and tools like TensorFlow, PyTorch, and spaCy. Guidance on best practices for deploying and scaling AI solutions in real-world environments Basic understanding of AI concepts such as supervised and unsupervised learning, classification, clustering, and neural networks. AI ethics and responsible AI development practices. Python proficiency is essential. Python libraries like NumPy, pandas, and matplotlib for data manipulation and visualization. version control systems like Git. Large Language Models (LLMs) and their use cases.
