Nathan Sifuna

Designed and deployed scalable backend systems supporting AI model evaluation workflows and distributed processing. Built RESTful APIs and full-stack applications and contributed to AI output evaluation, prompt optimization, and backend performance improvements.

Developed internal dashboards and evaluation tools for AI model assessment workflows. Built backend services and data pipelines for grading and annotation systems, implementing caching and optimizing data processing performance.

Engineered backend services and processing pipelines for large-scale AI data systems. Optimized distributed processing workflows and supported AI training infrastructure and software evaluation initiatives.

Developed and deployed NLP and machine learning applications in production. Built APIs and scalable services for model serving and real-time processing while improving reliability and performance through testing and optimization.

PhD research in scalable AI systems, distributed architectures, and machine learning optimization. Dissertation focused on design and optimization of large-scale AI-driven systems.

MSc in Software Engineering with specialization in machine learning, cloud computing, and system design. Coursework emphasized building and designing software systems with an ML and cloud focus.

BSc in Software Engineering focusing on algorithms, data structures, and full-stack development. Built foundational skills for software engineering and scalable application development.