Job Description

University of Doha for Science and Technology (UDST) was officially established by the Emiri Decision No13 of 2022, and it is the first national university specializing in academic applied, technical, and professional education in the State of Qatar. UDST has over 70 bachelor's and master's degree programs, diplomas, and certificates. The university houses 5 colleges: The College of Business, the College of Computing and Information Technology, the College of Engineering and Technology, the College of Health Sciences, and the College of General Education, in addition to specialized training centers for individuals and companies. UDST is recognized for its student-centered learning and state-of-the-art facilities. Its world-renowned faculty and researchers work on developing the students' skills and help raise well-equipped graduates who proudly serve different sectors of the economy and contribute to achieving human, social, and economic development goals nationally and internationally. With more than 700 staff and over 8,000 students, UDST is the destination of choice for applied and experiential learning. The University is recognized for its student-centered learning and state-of-the-art facilities. Our faculty are committed to delivering pedagogically-sound learning experiences that incorporate innovative learning technologies. Our aim is to enhance students' skills and help develop talented graduates who can effectively contribute to a knowledge-based economy and make Qatar's National Vision 2030 a reality. The Applied and Experiential Learning Department is inviting nominations and applications for the position of Senior AI/ML Specialist - Intelligent Tutoring System Reporting to the Academic Manager of Applied & Experiential Learning Department, the incumbent will be in charge of the development and optimization of an advanced Intelligent Tutoring System (ITS) powered by GPT-based AI technologies. This role involves designing, implementing, and fine-tuning machine learning models to deliver personalized educational experiences to students Responsibilities

The ideal candidate will Design, develop, and integrate large language models (LLMs) into the ITS, ensuring optimal performance and user experience. The successful candidate will Customize and fine-tune GPT-based models for specific educational applications, enhancing their accuracy and relevance. The successful candidate will Handle large datasets, ensuring proper storage, access, and preprocessing for training and refining models. The ideal candidate will Continuously monitor and evaluate the performance of integrated models, implementing optimization techniques to enhance efficiency and response times and work closely with backend developers and educational technologists to ensure seamless integration of AI models with the ITS infrastructure and Learning Management Systems (LMS). The successful candidate will Create and maintain comprehensive technical documentation for model architectures, training processes, and integration methodologies.

Technical Competencies: Programming Languages: Proficiency in Python and experience with machine learning frameworks such as TensorFlow and PyTorch. Large Language Models: Experience with GPT-based models and familiarity with APIs from providers like OpenAI, Anthropic and HuggingFace. Natural Language Processing: Strong understanding of NLP techniques and their application in educational technologies. Data Preprocessing: Experience with data preprocessing, feature engineering, and model evaluation techniques. Cloud Platforms: Familiarity with cloud platforms, particularly Microsoft Azure, for deploying and managing AI models. Containerization: Knowledge of containerization tools like Docker. Orchestration Platforms: Experience with orchestration platforms such as Kubernetes. Database Management: Understanding of database management systems, including SQL and NoSQL databases. Learning Management Systems Integration: Experience with LMS integration and standards such as SCORM, xAPI, or LTI is a plus. Context Caching: Understanding of context caching mechanisms to enhance model efficiency and reduce computational costs. Local Knowledge Search: Ability to implement local knowledge search functionalities to provide contextually relevant information within the ITS. Training Techniques: Familiarity with training techniques such as one-shot and few-shot learning to improve model adaptability with minimal data.

Qualifications

Education: Bachelor's degree or higher in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Experience: Minimum of 5 years in AI/ML engineering, with a proven track record in developing and deploying large language models.

Languages: Fluency in written and spoken English language is required Fluency in written and spoken Arabic language is preferred

Temporary