Experience

Education

MSc Computer Science

University of Victoria · Present

Supervisor: Dr. Ashery Mblinyi

Research focus on continual learning and efficient neural architectures for resource-constrained deployment.

BSc Software Development

KCA University, Nairobi, Kenya · 2018–2022

Second Class Upper Division

Professional and Research Experience

AI Engineer

International Board of Ethics in AI (IBEA)

AI Ethics and Safety · 2025–Present

  • Leading development of Ethics API framework for AI model auditing
  • Implementing automated safety and fairness evaluation systems
  • Developing explainability tools for open-source AI models
  • Contributing to ethical AI standards and best practices

Chief Technology Officer

Vociply LTD

Research Leadership · 2024–Present

  • Spearheading R&D of AI-powered voice systems with multilingual NLU support
  • Optimizing voice AI systems for African markets and edge deployment
  • Leading engineering team in production ML infrastructure development
  • Establishing research protocols and deployment best practices

Software Engineer

Intelliverse AI

Applied ML Engineering · 2022–2024

  • Designed and deployed transformer-based automation systems for production environments
  • Built scalable ML pipelines using FastAPI, Docker, and AWS SageMaker
  • Optimized inference performance and system reliability for production workloads
  • Maintained comprehensive technical documentation and deployment procedures

Technical Expertise

Machine Learning & Research

ML Frameworks: PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers

Edge & Inference: ARM Compute Library, ONNX Runtime, Llama.cpp, TensorRT, OpenVINO

Programming: Python, JavaScript

Research Focus: Continual learning, model compression, quantization, edge AI

Infrastructure & Deployment

Cloud Platforms: AWS (SageMaker, Lambda, EC2), GCP, Azure

MLOps: Docker, Kubernetes, Git, CI/CD pipelines

Production ML: FastAPI, scalable ML systems, edge deployment

Tools: Version control, collaborative development, reproducible research