Hello, world -- visitors

Hamza Khan

Cloud & DevOps Engineer

Naperville, IL

About Me

I’m Hamza Khan, a Computer Science student with a strong interest in cloud computing, AI, and building scalable systems. I’m driven by the challenge of turning complex problems into practical, real-world solutions, especially through modern cloud technologies. Whether it’s designing backend systems or exploring new tools, I enjoy learning how different pieces of technology come together to create impactful products.

I have experience working across full-stack development, cloud infrastructure, and backend systems, with a growing focus on AI-driven applications. I’ve built projects using tools like React, Flask, and AWS, and have worked in professional environments where I contributed to real-world engineering problems. I enjoy solving problems that require both technical depth and creative thinking, and I’m especially interested in systems that need to scale efficiently and reliably.

Right now, I’m focusing on deepening my skills in cloud architecture, DevOps, and AI integration. I’m actively building projects that combine these areas and looking for opportunities where I can contribute to impactful systems while continuing to grow as an engineer.

Experience

Storage Infrastructure Engineer Intern

Options Clearing Corporation (OCC)

May 2026 — Present

Chicago, IL

Cloud Engineer Intern

Norfolk Southern Corporation

Jan 2026 — Aug 2026

Atlanta, GA

  • Built an automated AWS tag compliance system using Lambda, DynamoDB, and SES to scan EC2 resources daily and notify teams of missing tags. This reduced manual auditing efforts and decreased untagged instances by approximately 25–40%.
  • Created reusable Terraform modules integrated with AWS Controllers for Kubernetes (ACK), enabling teams to provision cloud resources like S3 and ALBs through ArgoCD with consistent security and configuration standards.

Research Intern

University of Illinois Chicago

May 2024 - Aug 2024

Chicago, IL

  • Conducted a comparative analysis of over six machine learning algorithms applied to power systems, helping identify the most effective approaches for forecasting, optimization, and fault detection.
  • Evaluated a range of supervised, unsupervised, and deep learning models, contributing to research on improving performance and reliability in power system applications.

Education & Certifications

B.S. in Computer Science

University of Illinois Chicago

2023 — 2027 (Expected)

Relevant coursework: Data Structures, Systems Programming, Machine Organization, Software Design
GPA: 3.60/4.0

AWS Certified Cloud Practitioner

Amazon Web Services

Issued Feb 2026

Cloud Fundamentals & Core Services

AWS Certified Solutions Architect – Associate

Amazon Web Services

Issued Feb 2026

Cloud Architecture, Scalable Systems, Security & Cost Optimization

AWS Certified AI Practitioner

Amazon Web Services

Issued April 2026

AI/ML Fundamentals, Generative AI, Model Use Cases & Responsible AI

Skills

Cloud

AWS EC2 Lambda S3 CloudFront DynamoDB API Gateway IAM Route 53 CloudWatch

IaC & DevOps

Terraform GitHub Actions Docker CI/CD Kubernetes ArgoCD Jenkins Ansible

AI/ML

ML fundamentals Supervised & Unsupervised Learning Deep Learning Gen AI AWS Bedrock Model Evaluation Scikit-learn

Languages

Python JavaScript HTML / CSS SQL Java Bash

Tools

Git VS Code Linux AWS CLI