https://github.com/lukebrady/ai-engineering
AI Engineering in the open
https://github.com/lukebrady/ai-engineering
ai ai-agents ai-engineering
Last synced: 5 months ago
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AI Engineering in the open
- Host: GitHub
- URL: https://github.com/lukebrady/ai-engineering
- Owner: lukebrady
- Created: 2025-08-17T15:18:16.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-09-26T04:28:14.000Z (9 months ago)
- Last Synced: 2025-09-26T05:46:17.997Z (9 months ago)
- Topics: ai, ai-agents, ai-engineering
- Language: Python
- Homepage:
- Size: 366 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Agents: AGENTS.md
Awesome Lists containing this project
README
# AI Engineering
A comprehensive collection of AI engineering projects designed as an **executable codebase** - where both humans and AI agents can discover and run available tools through a unified interface. This repository demonstrates practical implementations across cloud infrastructure, AI inference systems, and graph-based AI architectures.
[](https://opentofu.org/)
[](https://packer.io/)
[](https://aws.amazon.com/)
## 🚀 Quick Start
```bash
# Get available commands and project overview
make help
# Check prerequisites (Packer, AWS CLI, OpenTofu)
make check-prerequisites
# Initialize and setup the project
make setup
```
## 📁 Project Structure
```text
ai-engineering/
├── Makefile # 🎯 EXECUTABLE INTERFACE - Universal tool catalog
├── README.md # This file
├── .github/workflows/ # CI/CD automation for tool validation
├── agents/ # AI agent implementations and examples
│ └── oss-agent/ # Example agent using open source models
├── infrastructure/ # Cloud infrastructure projects
│ └── ai-inference/ # Production-ready AI inference infrastructure
│ ├── packer/ # Custom Ubuntu 24.04 AMI builder with GPU support
│ └── opentofu/ # Modular Infrastructure as Code deployment
│ ├── iam/ # IAM roles and permissions
│ ├── inference/ # Main deployment using modular components
│ └── modules/ # Reusable Terraform modules
│ └── inference/ # Parameterized inference server module
└── intro-langgraph/ # (Planned) LangGraph learning project
```
## 🎯 Project Philosophy: Executable Codebase
This repository embodies a core principle: **the codebase itself should be executable by both humans and AI agents**. Every tool, command, and capability is discoverable and runnable through a unified interface.
### For AI Agents 🤖
```bash
make help # Same interface - AI agents can parse and execute tools
```
**Key Principles:**
- **Single Source of Truth**: The Makefile serves as the authoritative catalog of all executable operations
- **Self-Documenting**: Every command includes clear descriptions and usage examples
- **Universal Access**: The same interface works for humans, CI/CD systems, and AI agents
- **Discoverability**: No hidden commands - everything is accessible via `make help`
## 🎯 Project Goals
This repository serves as a comprehensive portfolio demonstrating:
- **Infrastructure as Code**: Automated cloud infrastructure provisioning
- **AI Inference Systems**: Production-ready AI model deployment
- **DevOps Best Practices**: CI/CD, automation, and monitoring
- **Human-AI Collaboration**: Interfaces designed for both human and AI agent interaction
- **Learning in Public**: Documented journey through AI engineering
## 🏗️ Current Projects
### 1. AI Inference Infrastructure (`infrastructure/ai-inference/`)
A complete infrastructure solution for deploying AI inference workloads on AWS using custom AMIs and modular OpenTofu configuration.
**Key Features:**
- Custom Ubuntu 24.04 AMI with Docker, NVIDIA drivers, and GPU support
- Modular architecture: separate IAM, inference, and reusable modules
- Multi-model deployment: Qwen 3 0.6B, GPT-OSS 20B, and Gemma 3 27B configurations
- vLLM server with systemd integration and container lifecycle management
- GPU-enabled instances (g5.2xlarge) with automated provisioning
- Comprehensive security: EBS encryption, restrictive security groups, IAM best practices
**Quick Deploy:**
```bash
# Build custom AMI
make ami-build
# Deploy IAM resources
make tofu-iam-apply
# Deploy inference infrastructure
make tofu-inference-apply
```
### 2. OSS Agent Example (`agents/oss-agent/`)
A practical example of building AI agents using open source models with comprehensive documentation.
**Key Features:**
- OpenAI-compatible API integration for local models (vLLM, Ollama)
- Function calling with Wikipedia search tools
- Interactive REPL with Rich formatting
- Comprehensive comments explaining agent patterns and OSS model usage
**Quick Start:**
```bash
cd agents/oss-agent
python main.py # Interactive agent with Wikipedia search
```
## 🛠️ Technologies
- **Infrastructure**: OpenTofu (Terraform), Packer, AWS (EC2, EBS, VPC, IAM)
- **AI/ML**: vLLM, Python, GPU acceleration, OpenAI-compatible APIs
- **Containerization**: Docker, systemd service management
- **Automation**: Make, Bash scripting, GitHub Actions
- **Security**: EBS encryption, security groups, IAM best practices
## 📋 Executable Tool Catalog
The project's **Makefile serves as the executable interface** - a programmatically parseable catalog of all available tools. This design enables both humans and AI agents to discover and execute operations using the same commands.
### 🔍 Tool Discovery
```bash
make help # Lists all available tools with descriptions
```
**Example Output:**
```text
AI Engineering - Executable Tool Catalog
AMI/Packer Commands:
ami-build Build the AMI (with validation)
ami-init Initialize Packer plugins
ami-validate Validate Packer configuration
Agent Commands:
agent-oss-check Check OSS agent environment and dependencies
agent-oss-install Install OSS agent dependencies
agent-oss-run Run the OSS agent interactively
Infrastructure/OpenTofu Commands:
tofu-apply Deploy all infrastructure with OpenTofu
tofu-iam-apply Deploy IAM infrastructure with OpenTofu
tofu-inference-apply Deploy inference infrastructure with OpenTofu
tofu-init Initialize all OpenTofu modules
tofu-plan Show deployment plan for all modules
tofu-validate Validate all OpenTofu modules
Setup & Verification Commands:
check-aws-config Check AWS configuration and permissions
check-prerequisites Check if required tools are installed
setup Complete setup and initialization
Utility Commands:
help Show this help message
list-ami List recent AMIs created by this project
status Show current project status
```
### 🤖 AI Agent Compatibility
The Makefile format is specifically designed to be:
- **Parseable**: AI agents can extract command names and descriptions
- **Executable**: Commands can be run programmatically
- **Self-Contained**: Each command includes all necessary context
- **Consistent**: Uniform pattern across all operations
### 📊 Tool Categories
**Infrastructure Commands:**
- `make ami-build` - Build custom Ubuntu AI inference AMI
- `make tofu-iam-apply` - Deploy IAM resources (roles, policies)
- `make tofu-inference-apply` - Deploy inference infrastructure (EC2, vLLM)
- `make tofu-apply` - Deploy all infrastructure (IAM + inference)
- `make tofu-destroy` - Tear down all infrastructure
**Agent Commands:**
- `make agent-oss-run` - Run the OSS agent interactively
- `make agent-oss-install` - Install OSS agent dependencies
- `make agent-oss-check` - Check agent environment and dependencies
**Utility Commands:**
- `make check-prerequisites` - Verify required tools
- `make setup` - Complete project initialization
- `make status` - Show current project status
- `make clean` - Clean build artifacts
**Validation Commands:**
- `make ami-validate` - Validate Packer configuration
- `make tofu-validate` - Validate OpenTofu configuration
## 🚧 Planned Projects
### 2. LangGraph Introduction (`intro-langgraph/`)
- Graph-based AI application development
- Multi-agent systems and workflows
- Integration with various LLM providers
### 3. Additional Infrastructure Components
- Container orchestration (EKS)
- Model serving platforms
- Monitoring and observability stack
## 🔧 Prerequisites
Before using this project, ensure you have:
1. **AWS CLI** configured with appropriate permissions
2. **Packer** 1.7+ for AMI building
3. **OpenTofu** 1.0+ for infrastructure deployment
4. **Make** for automation commands
Quick installation on macOS:
```bash
brew install awscli packer opentofu
```
## 📖 Documentation
Each subproject contains detailed documentation:
- [`infrastructure/ai-inference/packer/README.md`](infrastructure/ai-inference/packer/README.md) - AMI building guide
- [`infrastructure/ai-inference/opentofu/README.md`](infrastructure/ai-inference/opentofu/README.md) - Complete infrastructure deployment guide
- [`agents/oss-agent/main.py`](agents/oss-agent/main.py) - Comprehensive agent implementation with inline documentation
## 🤝 Contributing
This is a personal learning repository, but feedback and suggestions are welcome! Please:
1. Check existing issues and documentation
2. Open an issue for bugs or feature requests
3. Follow the established code and documentation patterns
## 🎓 Learning Resources
This repository represents practical implementations learned from:
- AWS Well-Architected Framework
- Infrastructure as Code best practices
- AI/ML deployment patterns
- DevOps automation principles
---
*This project demonstrates production-ready infrastructure automation, AI system deployment, and continuous learning in the rapidly evolving field of AI engineering.*