AI Engineering Hub
Welcome to the central knowledge base for the AI Engineering team. This site contains the guides, patterns, and reference material we use to design, build, and operate production-grade AI systems.
Topic Overview
Home
Central knowledge base for building, deploying, and evaluating production AI systems.
Python
6 items
LangChain
3 items
LangGraph
2 items
Azure AI Search
2 items
PostgreSQL
2 items
Azure Container Apps
2 items
Langfuse
2 items
CI/CD
2 items
Core Tech Stack
Technologies we use in production
| Category | Tools |
|---|---|
| Language & Tooling | Python 3.12+, uv, ruff, ty |
| LLM Frameworks | LangChain, LangGraph |
| Search & Retrieval | Azure AI Search (vector + hybrid) |
| Data & State | PostgreSQL (checkpointing), Redis (caching) |
| Deployment | Azure Container Apps, Docker |
| Observability | Langfuse (tracing, evals, golden datasets) |
| CI/CD | GitHub Actions, pre-commit hooks |
Getting Started
New to the team? Follow this path to get up to speed quickly:
- Set up your environment — Read Python Getting Started for tooling and project scaffolding.
- Understand our patterns — Browse LangChain and LangGraph to learn how we structure LLM applications.
- Learn the infrastructure — Review Azure AI Search, PostgreSQL, and Azure Container Apps for deployment context.
- Add observability — Integrate Langfuse into your workflows from day one.
- Automate everything — Follow the CI/CD guide to wire up testing and deployment pipelines.
Contributing
All documentation is version-controlled alongside the codebase. If you find gaps, outdated content, or want to add a new topic, open a PR against this repository. Keep articles practical, concise, and directly applicable to our work.