Systems Engineer | DevSecOps | Data & Machine Learning

Building resilient infrastructure, optimizing low-level systems, and engineering data pipelines from the ground up.

Currently focused on secure containerization, automated CI/CD workflows, and developing high-performance edge computing architectures for aerospace applications.

Infrastructure & Security (DevSecOps)

• Containers & Orchestration: Kubernetes, Docker, Docker Compose, Linux internals
• Automation: GitHub Actions, GitLab CI/CD, Zero-touch pipelines
• Security: Container hardening, dependency auditing, secure workflows

Systems & Performance (Core)

• Languages: C, C++98, Bash
• Focus: Memory management, raw pointers, memory leak tracking (Valgrind), legacy system maintenance

Data & Machine Learning

• Languages & Tools: Python, Pandas, NumPy, Scikit-learn
• Focus: Data cleaning, binary classification models, deterministic algorithms

• cubesat-dynamic-telemetry
  A schema-driven telemetry parsing engine designed to ingest, unpack, and convert binary space packets (like CCSDS/QB50) into human-readable data formats using Python and C++.
• 42AI
  Machine learning projects from 42 School, including logistic regression, neural network and computer vision
• 42-Core-Architecture
  A collection of low-level system engineering projects written in pure C/C++, focusing on strict memory safety, custom algorithm implementation, and high-performance computing without standard libraries.