TÜV SÜD – AI Quality Assessment Engineer Intern

We are seeking a motivated and innovative individual to join us as an AI Quality Assessment Engineer Intern. This exciting internship opportunity will provide the selected candidate with an enriching professional experience, the chance to work with cutting-edge AI systems and the opportunity to learn from experienced supervisors in one of the top TIC (testing, inspection, and certification) companies in the industry. 

Key Responsibilities: • Conduct comprehensive surveys of advanced AI system assessment methodologies and tools in the market. • Implement or reproduce the assessment methodologies, providing practical inputs for enhancing their efficiencies. • Perform assessment of client's AI services and continuously strive for optimizing outcomes. • Produce detailed and insightful reports based on AI service assessment, aiding the team in vital decision making. • Collaborate with the technical team to ensure optimal functioning of AI algorithms. • Demonstrate a continual learning attitude, staying up to date with the latest AI trends and technologies. 

Learning Outcomes: • This position promises significant learning opportunities which include but is not limited to: o Enhancement of your technical skills in the AI field, particularly related to quality assessment methodologies and tools. o An opportunity to have hands-on experience with state-of-the-art AI systems, and to analyse their operation. o The enhancement of your Python programming skills in a practical setting.o Gain in-depth insights into the operations of one of the best TIC industry players. • Opportunity to work in a dynamic and stimulating environment, honing your teamwork and project management skills. • Experience in preparing and presenting technical reports and insights to a professional audience. 

Key Requirements: • Passionate about novel technologies and keen to explore the technical realms. • Strong foundation in machine learning algorithms and data science. • Proficient in Python programming. • The candidate should be able to develop efficient and robust codes. • A proactive learner, willing to immerse themselves in exposure learning and adopt a hands-on approach to understanding machine learning frameworks. • Excellent writing and communication skills, capable of creating coherent and insightful reports .