Zaid Alshami

Designed and engineered high‑performance prompt strategies for large language models and led fine‑tuning initiatives with hyperparameter optimization to improve accuracy and efficiency. Applied quantization and knowledge distillation to enable scalable deployment of AI systems across production environments.

Built an AI‑powered quiz generation system using LangChain and OpenAI to automate question creation for 10K+ learners with 90% topic relevance. Implemented RAG pipelines for personalized learning content, boosting engagement and retention by 35%. Deployed end‑to‑end on AWS (SageMaker/Lambda/ECS) with a Flutter UI for cross‑platform access.

Delivered custom LLM solutions to global enterprise clients across 5+ industries, achieving 100% client satisfaction and on‑time deployments. Built scalable LLM agent frameworks handling thousands of concurrent requests, reducing inference latency by 30%. Developed RAG pipelines with millions of domain‑specific documents, improving answer accuracy by 40%. Implemented DL projects in PyTorch across CV/NLP pipelines and designed AI agents with LangGraph for automated reasoning, cutting manual intervention by 50%. Deployed containerized ML systems on AWS/Google Cloud with auto‑scaling and used Vertex AI Studio for experimentation, reducing iteration cycles by 35%. Applied LoRA/QLoRA fine‑tuning to speed up training with minimal accuracy loss and built AWS MLOps pipelines for continuous deployment.