Generative AI and Data Science Intern

FlexiSAF Edusoft Limited is a Software company that is focused primarily on education. Since its inception in 2010, FlexiSAF has continued to take giants strides in reinventing learning and inspiring people to make lifelong positive impact. ‍ With over 500 institutions, (primary, secondary, tertiary, government parastatals and learning centres), over 100 staff and 6 branches, everything we do has our clients at the epicentre.

Job Description At FlexiSAF, we are passionate about reinventing learning, nurturing talent, and inspiring individuals to make positive career decisions. We believe that data science and artificial intelligence (AI) are two of the most important and transformative fields of our time, and we are committed to helping individuals develop the skills and knowledge they need to succeed in these fields. Our Generative AI and Data Science program is designed to nurture the next generation of AI talents who will drive innovation and opportunities. It is designed to equip participants with the skills required to excel in this dynamic field. The program is divided into three levels: beginner, intermediate, and advanced. Each level covers a range of topics, from the basics of data science and AI to more advanced concepts such as machine learning, deep learning, and generative AI. Accepted Participants will learn based on a self-study approach, carry out tasks and complete projects in line with a well-structured and curated Internship Program Curriculum and resources which we provide to you at no cost, their progress will be monitored and supervised throughout the cohort to ensure that you remain on track and accountable. During the program, you will learn alongside innovative and inspiring leaders through mentorship and gain valuable experience working on real-life solutions while also acquiring some work-ready, soft-skills along the way. Requirements Familiarity or Working knowledge in at least one programming language such as Python and its associated libraries such as TensorFlow, PyTorch, or scikit-learnis required. Familiarity or Working knowledge of backend development principles, frameworks and technologies is essential. This includes experience with server-side programming languages such as Python, Java, or Node.js and frameworks such as Flask or Django, as well as proficiency in working with databases and RESTful APIs. Familiarity or Understanding of fundamental AI concepts and techniques such as machine learning, deep learning, neural networks and architectures, data modeling, computer vision, and NLP (Natural Language Processing). Familiarity with Data Manipulation and Analysis: Proficiency in data manipulation and analysis, applicants should have basic knowledge of large datasets, performing data preprocessing, feature engineering and data visualization using visualization tools such as Google Data Studio, Power BI, Tableau, e.t.c. Familiarity with AI Ethics and Bias. Accountable via set metrics and performance indicators Analytical and problem-solving skills. Communication Skills and Teamwork. Portfolio or Demonstrable Projects that demonstrate your skills and passion for AI development. This can include personal projects, academic projects, or contributions to open-source AI projects would be an advantage for participants joining at the Level 3 (Advanced stage). Benefits Mentorship from Industry Experts: Gain valuable insights and guidance from industry-leading AI and Data Science professionals who will provide mentorship during the program. Hands-on Projects: Apply your learning to real-world projects, providing you with practical experience and a strong portfolio. Access to Tools: Explore the latest AI and Data Science tools and technologies, including generative AI frameworks, to stay ahead of the curve. Career Advancement: Prepare for a successful career in AI and Data Science with our comprehensive program, opening doors to exciting opportunities. Networking Opportunities: Connect with a diverse and supportive community of fellow learners and professionals in the field.