Danyal Rehan

AI Developer & Software Engineer

About Danyal

Danyal is an AI Developer and Software Engineer who builds intelligent systems that actually work in production.

With a Bachelor's degree in Artificial Intelligence & Analytics from the University of Texas at Dallas, Danyal is well poised to make an impact in the AI space and leverage his skills to create innovative solutions that drive business value.

Some of the work he's done includes building agentic RAG assistants, designing multi-turn LangGraph pipelines, and shipping production REST APIs. He works across the full stack but gravitates toward where AI is doing real work.

Check out his projects below, or reach out.

Certifications

AI & Agentic Frameworks

Foundation: Introduction to LangGraph — Python

Claude Code in Action — Anthropic Academy

Claude 101 — Anthropic Academy

Cloud

AWS Certified AI Practitioner

AWS Certified Cloud Practitioner

Others

ISC2 Certified in Cybersecurity

IBM Python for Data Science, AI, and Development

Enterprise Design Thinking Practitioner — IBM

Experience

Toyota Motor North America

Software Engineer Co-Op

Designed and deployed an agentic RAG AI assistant for a Toyota web application, integrating LangGraph, OpenAI's Chat Completion API, and MongoDB vector search to deliver real-time, semantically accurate user support.
Engineered and shipped production REST APIs using Spring Boot behind AWS API Gateway, authoring environment-specific Postman test suites to enforce contract reliability across integrations.
Owned CI/CD pipeline management in GitLab, implementing feature branching strategies and automated build workflows that reduced manual deployment steps and accelerated release cycles.

AWS Organization at UTD

Technology Officer

Directed technical workshops for 100+ students covering EC2 instance setup, configuration, and SSH client connection.
Answered member inquiries about AWS services and certifications, providing mentorship and guidance.

Webacy

Security Data Analyst

Reduced risks of smart contracts by 90% through identification and labeling of vulnerabilities, enhancing the security protocols.
Delivered 800+ smart contract evaluations through the use of frequency and correlation analysis using 32 risk tags.
Streamlined department wide analysis by utilizing machine learning to reveal critical patterns in 800+ smart contracts.
Enhanced decision-making of the department by leveraging MS Excel and Python for data analysis, manipulation, and visualization.