Lewis Muguna

Led the design and architecture of scalable Generative AI solutions, including advanced Retrieval Augmented Generation (RAG) and autonomous agent systems, to enhance digital customer experiences and internal operational efficiency. Spearheaded the rapid prototyping of novel GenAI applications and drove their evolution into secure, production-grade systems, focusing on performance, reliability, and integration with existing enterprise platforms. Directed the fine-tuning of Large Language Models (LLMs) on proprietary company data to improve accuracy and relevance for specialized business use cases, establishing new performance benchmarks for domain-specific tasks. Established and championed best practices for prompt engineering, model evaluation, and responsible AI deployment, mentoring cross-functional teams to elevate the company's overall Generative AI capabilities.

Architected, developed, and deployed end-to-end, production-grade AI systems, taking

ownership of the full lifecycle from data ingestion and model development to secure API design

and cloud deployment.

Engineered and scaled data-intensive, full-stack applications using Python (FastAPI, Flask),

implementing clean, asynchronous, and RESTful APIs to serve real-time machine learning

inference.

Designed and built sophisticated Generative AI applications, including a production-ready

Retrieval-Augmented Generation (RAG) system for veriable document analysis and a multi-agent

framework for robust LLM function-call validation.

Implemented comprehensive MLOps pipelines using Docker, CI/CD (GitHub Actions), and

Infrastructure as Code (Terraform) on GCP and AWS, automating model training and deployment.

Engineered a secure, user-facing AI web automation tool from scratch, implementing a robust

OAuth2/JWT authentication ow to build secure, multi-tenant AI products.

Mastered the data layer for AI applications, designing data models and optimizing high-trac

PostgreSQL and Redis databases to ensure low-latency performance and scalability.

Model Evaluation: Reviewed and scored AI-generated responses, providing structured feedback through A/B testing, ranking, scoring, and annotation. Prompt Engineering: Specialized in prompt design and tuning, crafting precise instructions to guide models for coherent and accurate outputs. RLHF & Data Curation: Refined model behavior by rating and editing responses and performing fact verification to ensure accuracy. Safety & Alignment: Proactively flagged content for safety issues (toxicity, hallucinations) and conducted red teaming to address model vulnerabilities.