Systems & Machine Learning Engineer
Performance-Aware ML · High-Performance Computing · Cloud & Security

I build research-driven, performance-aware systems where correctness, scalability, and real constraints matter.
My background spans computer science, mathematics, and cybersecurity, and my work sits at the intersection of machine learning, systems engineering, and applied computation.

I’m comfortable operating across research prototypes and production-minded systems, and I focus heavily on understanding why systems fail, where models break, and how architecture shapes outcomes.

Rather than listing repositories, the sections below describe the systems I’ve built and the problems they address. Each system is backed by one or more repositories.

Noise-Robust Hybrid Quantum Neural Networks (Master’s Thesis, ECU)

I conducted a research-grade experimental study on hybrid quantum-classical neural networks (HQNNs) under realistic noisy intermediate-scale quantum (NISQ) conditions.

Instead of treating quantum noise as an afterthought, this work:

• Explicitly models noise at the system and optimizer level
• Compares hybrid architectures against classical ML baselines
• Evaluates stability, convergence, and failure modes across frameworks

I built a 13-demo experimental ecosystem using Qiskit, Cirq, and PennyLane, and showed that:

• Localized, noise-aware hybrid architectures degrade gracefully
• Globally entangled quantum models collapse under realistic noise
• Many reported quantum ML advantages disappear when evaluated honestly

This work emphasizes reproducibility, negative results, and architectural realism.

I build performance-critical software where architectural choices matter more than algorithms alone.

My HPC work includes:

• CUDA-accelerated particle simulations with CPU vs GPU benchmarking
• MPI-based distributed Monte Carlo systems executed under SLURM
• Analysis of memory access patterns, communication overhead, and scaling limits

These systems were executed on production HPC infrastructure and focus on how computation behaves under real hardware constraints, not just theoretical speedups.

I design end-to-end ML systems that go beyond notebooks.

This includes:

• Time-series forecasting pipelines for financial and quantitative data
• Feature engineering grounded in mathematical reasoning
• Inference optimization (including ONNX-based deployment)
• Evaluation under non-ideal data and operational constraints

The emphasis is always on deployment readiness, monitoring, and failure awareness, not leaderboard scores.

I’ve built and studied systems where security and correctness are first-class concerns, including:

• Backend and frontend architectures with clear trust boundaries
• Blockchain-based identity verification primitives
• Secure data handling and access control considerations
• Infrastructure designed with observability and auditability in mind

• M.S. Computer Science — East Carolina University
• M.S. Cybersecurity — North Carolina A&T State University (in progress)
• M.S. Mathematics — North Carolina Central University (in progress)
• B.S. Mathematics — North Carolina Wesleyan University

This academic path reflects a deliberate blend of:

• Mathematical foundations (algebra, probability, analysis)
• Systems and architecture (OS, networking, HPC)
• Machine learning and data systems
• Security, privacy, and policy

My certifications reinforce implementation depth, not surface knowledge.

They support:

• Cloud & Infrastructure (AWS, Azure, Kubernetes, Terraform)
• DevOps / MLOps (CI/CD, SageMaker, Azure ML, deployment pipelines)
• Advanced ML & Statistics (linear models, biostatistics, ML evaluation)
• Security & Emerging Topics (cybersecurity tools, AI, quantum information)

These are applied directly in my systems work rather than treated as standalone achievements.

Languages & Low-Level Systems

• Python, C++, CUDA, MPI, SQL, Linux

Machine Learning & Data

• PyTorch, ONNX, NumPy, Pandas, scikit-learn, Jupyter

Infrastructure & Platforms

• Docker, Kubernetes, Terraform
• AWS, Azure
• CI/CD, MLOps
• OpenShift, VMware, vSphere

Foundations

• High-Performance Computing
• Distributed Systems
• Secure System Design
• Quantitative & Statistical Analysis

LinkedIn: https://www.linkedin.com/in/jesusrgil
GitHub: https://github.com/jeragilo