You are joining a company that deploys AI-powered video analytics to customer sites. Edge devices at each site capture and process video feeds from IP cameras, then upload results to the AWS cloud for further analysis, storage, and customer-facing dashboards.

Your job is to build, fix, and configure the infrastructure that makes this work — from Terraform and Kubernetes to edge device provisioning, networking, and CI/CD.

4 hours — You do not need to complete every module. Focus on demonstrating depth and quality over breadth.

The assessment consists of 5 modules (100 points total):

1. Clone this repo and create a private copy (see Submission below)
2. Read docs/GETTING_STARTED.md for setup instructions
3. Read docs/ARCHITECTURE.md to understand the company's infrastructure
4. Work through modules in submission/ — each file has instructions and TODOs
5. Reference data in data/ as needed (read-only, do not modify)

A syntax checker is included so you can validate your files before submitting:

pip install -r requirements.txt
python -m check                      # Check all files
python -m check --module terraform   # Check one module

This verifies your files parse correctly (HCL, YAML, Python, shell). It does not score your work — full evaluation is done by the hiring team.

See EVALUATION.md for the scoring rubric and what we're evaluating.

• Practical, production-ready solutions — not textbook answers
• Security-first thinking — least privilege, network segmentation, no open defaults
• Operational awareness — monitoring, alerting, rollback plans
• Clear communication — your markdown responses should be concise and actionable
• Trade-off reasoning — explain why you chose an approach, not just what you did

1. Create a private repository on your GitHub account
2. Push your completed work to the private repo
3. Add the following GitHub users as collaborators: (provided in email)
4. Reply to the assessment email with your repo URL

Do not fork this repo publicly.

• You may use any reference material, documentation, or AI tools
• Do not modify anything in check/ or data/
• All work should be in the submission/ directory
• Commit frequently — we review git history

If anything is unclear, email the hiring team. We'd rather clarify than have you guess.