A master's or PhD in Computer Science, Data Science, Artificial Intelligence, or a related field is typically preferred.
Relevant Certifications:
Industry-recognized certifications (e.g., TensorFlow Developer Certificate, Microsoft AI certifications, etc.) can be an added advantage.
Technical Expertise
Machine Learning & Deep Learning:
In-depth knowledge of machine learning algorithms, neural networks, deep learning architectures, and reinforcement learning.
Programming Skills:
Proficiency in languages such as Python (and possibly R) with familiarity in libraries like TensorFlow, PyTorch, scikit-learn, and Keras.
Data Handling:
Strong understanding of data preprocessing, data visualization, and statistical analysis.
Specializations:
Exposure to specialized areas such as Natural Language Processing (NLP), Computer Vision, or robotics can be beneficial.
Practical Experience
Industry Exposure:
Hands-on experience with real-world AI projects, from concept to deployment, demonstrating the ability to solve practical problems.
Project Portfolio:
A well-documented portfolio showcasing projects, research, or implementations in various AI domains.
Cloud Platforms:
Experience with deploying AI models using cloud services (AWS, Google Cloud, Azure) is often valued.
Training and Pedagogical Skills
Curriculum Development:
Ability to design and deliver a structured, comprehensive curriculum tailored to different skill levels.
Communication:
Strong verbal and written communication skills to break down complex concepts for diverse audiences.
Mentorship:
Experience in mentoring or coaching learners, ideally in a classroom or corporate training setting.
Soft Skills
Problem-Solving:
Critical thinking and a proactive approach to troubleshooting and innovation.
Adaptability:
Ability to stay current with rapidly evolving AI technologies and adjust training materials accordingly.
Collaboration:
Team-oriented mindset to work effectively with curriculum developers, other trainers, and industry partners.
Job Types: Full-time, Part-time