• View Portfolio | For Recruiters | For Consulting Clients | Performance Benchmarks | Technology Comparisons

Hi, I'm Drew— a DevOps Architect specializing in AI/ML infrastructure and Kubernetes operations.

What you'll find here:

• Production-grade infrastructure code and architectural patterns
• Multi-cloud Kubernetes deployment patterns (50+ implementations)
• AI/ML infrastructure with deep Nvidia GPU expertise
• Open-source tools and automation frameworks
• Comprehensive development patterns and developer experience automation

My Focus:

• Cloud infrastructure optimization (50%+ cost reduction typical for clients)
• Production-ready Kubernetes and multi-cloud deployments with NVIDIA GPU orchestration
• LLM deployment, GPU orchestration, and model serving on NVIDIA infrastructure
• Infrastructure as Code with GitOps principles
• Multi-agent AI systems and Model Context Protocol (MCP) integration
• Security-hardened supply chains (SLSA, immutable infrastructure)
• Cross-platform developer tooling and automation

Philosophy: Unix philosophy, GNU ethos, cypherpunk minded, polymath of the old school. I build systems that are composable, reproducible, and respect the principles of least surprise and maximum transparency.

Thousands of Hours of Nvidia Training and Practice

This portfolio demonstrates practical application of Nvidia technologies across multiple projects:

Certifications: Multiple Nvidia Deep Learning and GPU Computing certifications

Looking for engineers with Nvidia expertise? My code demonstrates hands-on production experience.

Why Consider This Portfolio?

Deep Technical Expertise:

• Nvidia Technologies: Production experience with GPU-optimized infrastructure
• Kubernetes: 50+ deployment patterns across AWS, GCP, Azure
• AI/ML Infrastructure: Production LLM deployments, MLOps pipelines
• Modern IaC: Pulumi (Go), Terraform, GitOps practices

Open Source Contributions:

• RegicideOS — AI-native Rust Linux distribution
• Merlin — LLM router with reinforcement learning (Rust)
• efrit — Native elisp coding agent
• Voice of the Dead — SOTA text-to-speech

Technical Skills Demonstrated:

Cloud & Infrastructure:

AI & ML:

Languages & Tools:

Contact for Recruiting:

• 🐙 GitHub Issues — Create an issue to reach out
• 📧 Use GitHub's email contact feature (if public on my profile)

I help organizations:

• Reduce cloud costs by up to 50%
• Accelerate AI/ML infrastructure deployment
• Migrate to Kubernetes with zero downtime
• Build production-ready MLOps pipelines

Proven Results:

"Reduced our AI costs by 60% while improving performance. The infrastructure overhaul was seamless and team training was invaluable." — CTO, FinTech Startup

"Helped us transition from legacy systems to Kubernetes with zero downtime. The migration strategy was brilliant and execution flawless." — VP Engineering, SaaS Company

How to Work With Me:

• Initial Consultation: Free 10-minute discovery call
• Engagement Models:
  • Tier 1: Strategy & Planning — $250/hr, 10-hour minimum
    • Infrastructure Assessment
    • Cost Optimization Analysis
    • Technology Roadmap
    • Team Training
  • Tier 2: Full Implementation — $5,000/project, exclusive to one client
    • Complete Infrastructure Overhaul
    • AI/ML Pipeline Development
    • Kubernetes Migration
    • Ongoing Support (retainer-based)

📅 Schedule Free Consultation: cal.com/aiconsulting

What You Get:

• Production-ready code (see demos in this repo)
• Knowledge transfer and team training
• Ongoing support and optimization
• Transparent pricing and clear timelines

Practical Impact:

"The best consulting delivers value that lasts long after the engagement ends. My focus isn't just solving today's problems—it's building systems and teams that can solve tomorrow's problems independently."

Enterprise-Grade Practices: The patterns demonstrated in this portfolio aren't just for startups—they scale:

• Compliance-Ready: SLSA Level 2/3 supply chain hardening for regulated industries
• Multi-Tenant Security: Zero-trust architecture with proper RBAC and isolation
• Audit Trails: Comprehensive logging, monitoring, and traceability across all systems
• Disaster Recovery: Immutable infrastructure with backup and restore strategies
• Scalable Architecture: Horizontal scaling with proper state management and orchestration

Production-Grade Infrastructure Patterns & Demos

# Clone the repository
git clone https://github.com/awdemos/demos.git
cd demos

# Explore available demos
ls -la demos/

Enterprise-Grade AI Stack

Production experience building and operating complete AI/ML infrastructure:

• NVIDIA GPU Operator: Automated GPU provisioning in Kubernetes
• MIG (Multi-Instance GPU): Partitioning for multi-tenant efficiency
• DCGM Monitoring: Real-time GPU metrics and telemetry
• CUDA Toolkit 12.1.0: Optimized workflows and memory management
• NVIDIA Container Toolkit: Seamless GPU access in containers

• Triton Inference Server: Production model serving with GPU acceleration
• MLflow: Experiment tracking, model registry, and lineage
• Ray: Distributed computing for training and inference
• Argo Workflows: ML pipeline orchestration with GitOps

• Model Serving: Production deployments (Mistral, OpenAI, custom models)
• Rust-First ML: Burn framework for memory-safe ML workloads
• Inference Optimization: TensorRT, batch processing, resource management

Explore the Demos:

• demos/llm/ — LLM infrastructure with GPU optimization
• demos/kubernetes/ — GPU-enabled Kubernetes deployments

Always Learning, Always Building

Active areas of investigation and experimentation:

• MCP (Model Context Protocol) — Building custom tools for AI agents
• Parallel Agent Workflows — Running multiple AI agents simultaneously for complex tasks
• Async Agent Coordination — Background task management and result aggregation
• Container-Isolated Environments — Safe execution of AI-generated code

• AI-Native Tools — Editors and IDEs with LLM-first design
• Automated Code Review — Using AI for architecture validation
• Self-Healing Infrastructure — Systems that detect and fix issues autonomously

• GPU Optimization — CUDA kernels, memory management, and NVIDIA TensorRT acceleration
• NVIDIA DCGM Integration — Deep GPU monitoring and telemetry for production systems
• Rust-Based AI Infrastructure — Performance-critical ML tooling
• Resource Scheduling — Efficient NVIDIA GPU allocation for multi-tenant systems

• SLSA in Production — End-to-end supply chain verification
• Zero-Knowledge Workloads — Confidential AI on untrusted infrastructure
• Hardened Container Images — Minimal attack surfaces for AI services

Want to Collaborate? These areas are actively evolving. If you're working on similar problems or want to explore together, let's connect.

• efrit — Native elisp coding agent running in Emacs. Nushell port in progress.
• Voice of the Dead — SOTA TTS project
• Merlin — LLM router written in Rust. Utilizes RL to route LLM prompts intelligently. GPL 3.0 project.

• RegicideOS — AI-native, Rust-first Linux distribution based on Gentoo, BtrFS, Cosmic-Desktop
• DCAP — Dynamic Configuration and Application Platform for distributed systems

• symbolic_ai_elisp_knowledge_base — Open-source reimagining of a Cyc-style knowledge base

• Dotfiles — Complete development environment with 300+ lines of Makefile automation, cross-platform support (macOS, Linux, WSL, Alpine), AI/ML stack integration, and comprehensive documentation

• container-use integration — Isolated development environments for AI coding agents with branch isolation and diff/review workflows
• MCP Servers — Production examples extending AI agents with custom tools (CLI execution, API integration, web search)

• Talos — Best in class Kubernetes OS
• Pulumi — Infrastructure as Code in general purpose programming languages
• vCluster — Virtual Kubernetes clusters
• Cilium — eBPF-based networking and security
• Cloudflare — Cost-effective cloud services
• Railway — Instant deployments, effortless scale

• GPTScript — Natural language scripting
• Claude Code — I use it daily
• pairup — AI Pair Programming in Neovim
• ComfyUI — Stable diffusion framework

• container-use — Isolated development environments for AI agents (Dagger)
• bincapz — Container image security analysis
• Colima — Container runtime for macOS/Linux
• Dive — Docker image layer analysis
• Podman — Daemonless container engine
• nerdctl — Docker-compatible containerd CLI
• slim — Container image optimization (30x reduction)

• Tekton — Cloud-native CI/CD framework
• Dagger.io — Programmable deployment pipelines

• Kitty Terminal — Fast, GPU-accelerated terminal
• Cursor IDE — AI-powered development environment
• Devcontainer — Containerized development
• Devpod — Automated dev environments

• Chainguard — Software supply chain security and minimal base images
• SLSA Framework — Supply chain Levels for Software Artifacts (implemented in dotfiles)
• GrapheneOS — Security-focused Android distribution
• NitroPC — Open-source secure PC

For Recruiters:

• 📧 Use GitHub's email (if public) or create an issue to reach out
• 📋 Review the Featured Projects for evidence of expertise

For Consulting:

• 📅 Schedule Free Consultation
• 💼 Review the For Consulting Clients section

Open Source:

• 🐙 Follow on GitHub for new projects
• ⭐ Star interesting projects to show appreciation

Open Source by Default

Everything in this portfolio is open source, documented, and reproducible. I believe in:

• Transparent systems - No black boxes, all decisions documented
• Knowledge sharing - Comprehensive guides and troubleshooting documentation
• Composable tools - Every component replaceable and well-integrated
• Security-first - SLSA implementation, immutable infrastructure, supply chain integrity

• Dotfiles Repository — Complete development environment with AI/ML stack, GPU orchestration, MCP servers, and advanced developer tooling
• MCP Guide — Comprehensive Model Context Protocol implementation examples
• AI Coding Tools — Terminal-focused AI assistance workflows
• SLSA Implementation — Supply chain security hardening
• Technology Comparisons — Deep analysis of Kubernetes, LLM serving, IaC, CI/CD, and service mesh tools
• Performance Benchmarks — Quantifiable metrics from production deployments
• Screenshots Guide — Instructions for creating visual assets to showcase demo projects

• Production-ready infrastructure patterns from real deployments
• Security best practices (SLSA Level 2/3, immutable infrastructure)
• Multi-agent AI system architectures
• Cross-platform developer experience automation

This portfolio and the associated dotfiles repository aren't just demos—they represent production patterns that solve actual problems:

• Cost Reduction: NVIDIA GPU scheduling and MIG partitioning that cut AI infrastructure spend by 50%+
• Reliability: GitOps workflows that have maintained 99.9%+ uptime across multiple clients
• Velocity: Automated CI/CD pipelines that reduced deployment times from hours to minutes
• Performance: CUDA optimization and Triton Inference Server deployments that improved inference throughput by 3-5x
• Security: SLSA implementation that passed external audits for regulated industries

Open Source ≠ Only Open Source While this repository contains openly available tools, patterns, and examples, the expertise demonstrated here is equally applicable to proprietary, confidential, or regulated environments. The principles—automation, reproducibility, transparency—work everywhere.

• Multi-Agent Orchestration: Building systems where AI agents collaborate with domain experts
• Self-Healing Infrastructure: Systems that detect and remediate issues autonomously
• AI-Native Tooling: Development environments optimized for AI-assisted workflows
• Quantum-Resistant Cryptography: Preparing infrastructure for post-quantum security requirements
• Distributed Training at Scale: Optimizing ML pipelines across heterogeneous NVIDIA GPU clusters
• CUDA Kernel Development: Custom GPU kernels for specialized AI workloads
• NVIDIA MIG Optimization: Advanced GPU partitioning strategies for multi-tenant efficiency

Beyond Basic Automation

Building systems that improve themselves:

• AAS (Artificial Age Score) Monad Framework — Mathematically-grounded scoring for configuration evolution, contradiction detection, and guided optimization
• Parallel Experimentation — Multiple isolated configurations evaluated objectively for optimal states
• Appetition-Driven Updates — Systems that evolve toward better configurations through measurable feedback

• Git Worktree + container-use — Parallel feature development with isolated AI agent environments
• Workmux — Project-based tmux session management with automatic workspace setup
• Multi-Agent Coordination — Multiple AI agents working simultaneously in isolated environments

• Immutable Infrastructure — All infrastructure declarative and version-controlled
• Security-First Design — SLSA Level 2/3 implementation, supply chain integrity
• Observability — Comprehensive monitoring (NVIDIA DCGM, MLflow, Prometheus dashboards)

Read More:

• Dotfiles AAS Framework
• Immutable Infrastructure Principles

Production-Grade Security Practices

Building systems that are secure by design:

• SLSA Implementation — Full supply chain provenance for artifacts
• Verifiable Builds — Reproducible builds with attestation
• Dependency Verification — SBOM generation and vulnerability scanning

• Zero-Trust Networking — Cilium, eBPF-based security policies
• Secrets Management — HashiCorp Vault, Kubernetes secrets encryption
• Container Hardening — Chainguard images, bincapz security analysis

• Code Signing — GPG signing for all commits and releases
• Security Auditing — Regular penetration testing and dependency updates
• Compliance-Ready — Infrastructure designed for SOC2 and ISO27001

Tools Used:

• Chainguard — Software supply chain security
• bincapz — Container image security
• SLSA Framework — Supply chain security standards

Modern Productivity Stack

Tools and workflows that make development faster and more reliable:

• Neovim / AstroVim — Lua-configured, LSP-powered editing with AI integration
• Kitty Terminal — GPU-accelerated, multiplexed terminal workflow
• Tmux — Session management with workmux for project-based automation
• Zellij — Modern terminal workspace alternative

• Dagger — Programmable CI/CD pipelines (Go SDK)
• container-use — Isolated environments for AI agents and testing
• nerdctl / Podman — Daemonless container engines
• slim — 30x container image size reduction

• Rust-Based Ecosystem — Modern replacements for coreutils, fd, ripgrep, bat, zoxide
• Nushell — Data-focused shell with structured data manipulation
• Homebrew / Nix — Reproducible package management

• btop — Real-time system monitoring
• htop — Process management with GPU metrics
• glances — Web-based system monitoring
• lazydocker — Terminal UI for Docker/containerd

Why This Matters:

"Tools aren't just utilities—they're force multipliers. A well-configured development environment can save 2-3 hours per day through automation, faster feedback loops, and reduced context switching."

Read More:

• Dotfiles Tooling Stack
• AstroVim Configuration

While this is my demo repository, create an issue if you would like to connect with me further!

All original code in this repository is released under the MIT License. Third-party components may have different licenses — please refer to their respective documentation.

© 2025 — Portfolio demonstrating AI infrastructure expertise.

🚀 Let's build something amazing together!