AI Trainee

We are looking for a highly motivated and mathematically-inclined AI Trainee to join our team. This is an entry-level position designed for individuals with a passion for artificial intelligence and a strong foundation in mathematics. You will play a crucial role in building the next generation of AI systems by applying your analytical skills to real-world challenges. Responsibilities: Assist in the development and implementation of AI and Machine Learning models. Apply advanced mathematical and statistical concepts, including linear algebra, calculus, probability, and optimization theory, to solve complex problems. Help prepare and preprocess large datasets, ensuring their quality and integrity. Collaborate with senior team members to understand project requirements and translate them into technical tasks. Research and analyze various machine learning algorithms and techniques to identify the most effective solutions for specific challenges. Contribute to the evaluation and validation of AI models, and help interpret model outputs. Participate in code reviews and learn best practices for building scalable and efficient AI systems. Document the development process, model architecture, and experimental results. Stay updated with the latest research and developments in the field of AI and Machine Learning. Qualifications: Bachelor's or Master's degree in Mathematics, Statistics, Computer Science, or a related quantitative field. Demonstrably strong foundation in mathematics, particularly in areas like linear algebra, multivariable calculus, probability, and statistics. Familiarity with at least one programming language, preferably Python. Basic understanding of machine learning concepts and algorithms. Excellent problem-solving and analytical skills. A strong desire to learn and a proactive attitude. Excellent written and verbal communication skills. Ability to work effectively both independently and as part of a team. Preferred Qualifications: Coursework or projects involving machine learning, data science, or artificial intelligence. Experience with numerical libraries such as NumPy and SciPy. Familiarity with machine learning frameworks like scikit-learn, TensorFlow, or PyTorch is a plus. Experience with data visualization tools (e.g., Matplotlib, Seaborn).