Open to opportunities

Ahmed Issah

@ahmedissah

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Dynamic AI Engineer specializing in scalable AI systems and NLP.

Rwanda

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What I'm looking for

I seek a collaborative environment where I can leverage my AI expertise to drive innovation and contribute to impactful projects.

I am a dynamic and results-driven AI Engineer with a strong foundation in building scalable AI systems and deep learning applications. Currently pursuing a Master’s in Engineering Artificial Intelligence at Carnegie Mellon University, I have honed my expertise in machine learning, data analysis, and AI research. My academic journey has been complemented by hands-on experience in developing impactful AI-driven solutions.

During my time as a Machine Learning Researcher at Carnegie Mellon University, I designed and implemented a dynamic Knowledge Graph for the agricultural domain, integrating advanced NLP techniques and OpenAI's GPT-4 API. My projects, including an Automatic Speech Recognition model and an enhanced image-text retrieval framework, showcase my ability to leverage cutting-edge technologies to solve real-world problems. I thrive in multidisciplinary teams and am committed to delivering high-quality results under tight deadlines.

Experience

Work history, roles, and key accomplishments

Current

Graduate Teaching Assistant

Current

Carnegie Mellon University

Dec 2024 - Present (6 months)

Led technical workshops and office hours to support student projects and assignments. Contributed to developing course materials, including practical demonstrations of deep learning models.

Deep Learning Teaching Mentoring Curriculum Development Feedback

Graduate Teaching Assistant

Carnegie Mellon University

Sep 2024 - Dec 2024 (3 months)

Delivered and guided recitations on machine learning and data inference concepts. Supported students by clarifying complex topics and evaluating assignments.

Machine Learning Teaching Collaboration Evaluator

Machine Learning Researcher

Carnegie Mellon University

Sep 2024 - Dec 2024 (3 months)

Designed and implemented a dynamic Knowledge Graph (KG) for the agricultural domain, integrating OpenAI's GPT-4 API and advanced NLP techniques for context-aware, real-time information retrieval. Applied a BERT-based model from Hugging Face for Named Entity Recognition (NER) and relationship extraction, enhancing the KG's granularity and usability.

OpenAI GPT 4 API NLP BERT Named Entity Recognition Knowledge Graphs PyMuPDF NLTK PyVis

Education

Degrees, certifications, and relevant coursework

Carnegie Mellon University

Master of Science, Engineering Artificial Intelligence

Grade: 3.64 CQPA

Specialized in Applied Machine Learning, Deep Learning, and Data Analysis. Focused on building scalable AI systems and cutting-edge AI/NLP applications.