Responsibilities - Develop and implement machine learning models and algorithms to solve complex business problems and enhance AI-driven applications. - Collaborate with cross-functional teams, including data scientists and software engineers, to integrate AI solutions into existing products and services. - Conduct data analysis, preprocessing, and feature engineering to extract valuable insights from large datasets, improving model performance. - Stay updated on the latest AI and machine learning advancements, research papers, and tools, incorporating them into AI development projects. - Optimize AI models for performance, scalability, and efficiency, leveraging cloud-based resources and distributed computing frameworks. - Actively participate in code reviews and collaborative problem-solving sessions, contributing to the improvement of AI solutions and development processes. - Troubleshoot and resolve AI-related issues, ensuring the reliability and accuracy of AI-driven applications. Required Skills & Experience - Bachelor's Degree in Computer Science, Bachelors in Data Science or ML/AI is preferred. - Proven track record as an AI Engineer, with portfolio examples of successful machine learning or AI projects. - Proficiency in programming languages commonly used in AI, such as Python, R, or Java, and experience with ML frameworks like TensorFlow or PyTorch. - Familiarity with data preprocessing, feature engineering, and model evaluation techniques essential for machine learning projects. - Strong understanding of various machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and neural networks. - Hands-on experience with Natural Language Processing (NLP), Large Language Models, computer vision, or other AI subfields. - Solid grasp of mathematical concepts relevant to AI, such as linear algebra, calculus, and statistics. - Experience with version control systems like Git, enabling effective collaboration and code management. - Strong analytical and problem-solving skills, crucial for algorithm development and data analysis. - Exceptional communication skills, enabling effective collaboration with both technical teams and business stakeholders to translate AI capabilities into business value.