Machine Learning Instructor

As a Machine Learning Instructor at MIT Academy SDN BHD, you will play a vital role in educating and mentoring students in the field of machine learning and artificial intelligence. Your passion for teaching and strong technical expertise will be essential in delivering engaging lectures, guiding students through real-world projects, and staying updated with industry trends. Key Responsibilities: - Design, develop, and deliver engaging lectures, labs, and projects in Machine Learning and related topics. - Teach core concepts such as supervised and unsupervised learning, neural networks, natural language processing, computer vision, and model deployment. - Guide students through real-world projects, case studies, and capstone assignments. - Stay updated with the latest industry trends and incorporate them into teaching content. - Mentor students in career paths, research opportunities, and skill development. - Collaborate with other faculty to enhance the curriculum and learning outcomes. Required Qualifications: - Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, or related field (PhD preferred). - Minimum 2 years of teaching or industry experience in Machine Learning / AI. - Strong programming skills in Python and familiarity with ML libraries (TensorFlow, PyTorch, Scikit-learn, etc.). - Experience with data visualization, feature engineering, and model optimization. - Excellent communication and presentation skills. - Passion for teaching and mentoring students. Preferred Qualifications: - Industry certifications in AI/ML (e.g., TensorFlow Developer, AWS Machine Learning, etc.). - Experience with cloud platforms (AWS, GCP, Azure). - Prior experience teaching or mentoring in an educational institution. - Contribution to open-source ML projects or published research papers. In addition to the challenging and rewarding role of a Machine Learning Instructor, you will benefit from: - Competitive salary and benefits package. - Opportunity to work in a collaborative academic environment. - Access to modern labs, tools, and industry projects. - Professional growth through workshops, seminars, and research opportunities.,