AI Engineer | 3+ Years Experience | GenAI, Agentic AI & RAG Systems

AI Engineer with 3+ years of experience shipping production AI/ML systems for enterprise and financial clients. I specialize in GenAI, multi-agent orchestration, Retrieval-Augmented Generation (RAG), and LLM evaluation, with experience in responsible AI controls and model governance in regulated environments.

I build systems end-to-end, from document ingestion and retrieval to agent workflows, backend APIs, human-in-the-loop review, deployment, and observability. My toolkit includes Python, FastAPI, Flask, LangGraph, LangChain, LlamaIndex, CrewAI, n8n, MCP, PyTorch, scikit-learn, XGBoost, pgvector, Pinecone, RAGAS, MLflow, and Langfuse. I deploy on AWS and Azure using Docker and Kubernetes, and build interfaces with React, Next.js, and TypeScript.

Selected achievements:

• Built a multi-agent contract-intelligence pipeline with source-span verification, semantic caching, MCP tools, and release-gating evaluations.
• Delivered a voice-to-workflow system that reduced Business Analyst effort by 80-90% and update latency from 12 minutes to under 2 minutes.
• Developed banking document-intelligence pipelines achieving 96% OCR accuracy, 93% field precision, and 4x processing throughput.
• Designed validation workflows that eliminated approximately 70% of manual review volume while maintaining governance and audit requirements.
• Built production RAG evaluation and observability workflows that improved retrieval success from 15% to 70%.

Projects include DriveLine, a bilingual English/Arabic multi-agent conversational AI system for automotive service, and a production RAG retrieval and evaluation system.

Master of Science in Artificial Intelligence, with a Machine Learning concentration, from the University of North Texas (2025, GPA 4.0). NVIDIA Certified Professional: Agentic AI (May 2026).