James Makimii

Designed and deployed scalable backend systems supporting AI model evaluation workflows and distributed processing. Built REST APIs and full-stack applications with Python, FastAPI, Next.js, PostgreSQL, and Redis, improving API response performance and backend throughput for real-time AI systems.

Developed internal dashboards and evaluation tools for AI model assessment workflows, including grading and annotation pipelines. Implemented caching and optimized data processing performance using SQL databases and asynchronous workflows to support structured evaluation and debugging tasks.

Engineered backend services and processing pipelines for large-scale AI data systems, optimizing distributed workflows to improve system efficiency. Supported AI model training infrastructure and software evaluation initiatives through reliability-focused debugging and system optimization.

Developed and deployed NLP and machine learning applications in production, building APIs and scalable services for model serving and real-time processing. Improved the reliability and performance of AI pipelines through testing, optimization, and analytics workflows over structured datasets.

Activities and societies: Research: Scalable AI systems, distributed architectures, and ML optimization; Dissertation: Design and optimization of large-scale AI-driven systems.

Researching scalable AI systems, distributed architectures, and ML optimization. Dissertation focused on design and optimization of large-scale AI-driven systems.

Activities and societies: Specialization: Machine Learning, Cloud Computing, System Design.

Master’s program with specialization in machine learning, cloud computing, and system design.

Activities and societies: Focus: Algorithms, Data Structures, Full-Stack Development.

Studied algorithms, data structures, and full-stack development as part of the software engineering bachelor’s program.