Ghaymah GenAI Engineer — Technical Interview

Track: Generative AI / AI Agents Duration: 60 minutes Submission: GitPasha (push to your exam repo) Language: English or Arabic — your choice (Arabic is encouraged for the ecosystem questions)

Welcome 👋

Welcome to the Ghaymah technical interview. This is a hands-on, 60-minute exam — not a whiteboard Q&A. We want to see how you think, what you build, and how you work inside the Arabic tech ecosystem, because that is the ecosystem we build for and with.

You are interviewing to join the team that builds agents like Cumin — the AI agent that runs inside a compute sandbox (persistent containers + serverless functions + an object-store workspace) and works across data processing, scraping, conversion, and deployment. In this exam you will:

Introduce yourself to the Arabic tech ecosystem (Qabilah + Mortakaz).
Design a memory module for an agent like Cumin, and show how to use it.
Propose an Arabic TTS or STT model + dataset.
Sign up to Ghaymah, deploy a static website, and build a LangChain agent on Ghaymah's GenAI services.

What we are evaluating

Skill	Where it shows up
Arabic tech ecosystem awareness	Qabilah + Mortakaz tasks
Agent engineering intuition (memory, retrieval, storage trade-offs)	Q1
Arabic NLP depth (model + dataset selection)	Q2
Shipping — deploy something live	Q3 + Q4
Agent building with real tooling (LangChain)	Q5

The stack you will touch

Platform	What it is	URL
Qabilah (قبيلة)	Arabic tech social / community platform	https://qabilah.com
Mortakaz (مُرتكز)	Directory of ~686 Arab-built products	https://mortakaz.com
Ghaymah (غيمة)	Arabic cloud platform (PaaS)	https://ghaymah.systems
Ghaymah Dashboard	Where you sign up, get AI models, and deploy	https://deploy.ghaymah.systems

Time budget (60 min total)

Task	Folder	Budget
Read this README + set up	—	5 min
Qabilah profile + check-in	`common-qabilah/`	3 min
Mortakaz discovery + integration proposal	`common-mortakaz/`	10 min
Q1 — Cumin memory module design	`q1-cumin-memory/`	15 min
Q2 — Arabic TTS/STT model + dataset proposal	`q2-arabic-tts-stt/`	6 min
Q3 — Ghaymah signup	`q3-ghaymah-signup/`	4 min
Q4 — Deploy a static site on Ghaymah	`q4-static-site-deploy/`	8 min
Q5 — LangChain agent on Ghaymah's GenAI services	`q5-llm-agent/`	9 min
Total		≈ 60 min

⏱️ Be honest about your time. A focused, complete 60-minute submission beats a half-finished 2-hour one. We look at commit timestamps.

Rules

Work inside this repo. Each question has its own folder with a task description and an answer template. Fill in your answers in place.
Do not rename or move the question folders.
Never commit real credentials, tokens, or passwords — use placeholders like <REDACTED>.
For Q4, commit your actual site source under q4-static-site-deploy/site/; for Q5 under q5-llm-agent/.
You may use any public resources (docs, model cards, papers, blogs) — cite them inline.

Repository structure

ghaymah-genai-exam/
├── README.md                      ← you are here
├── common-qabilah/
│   └── qabilah-ecosystem.md       ← task + answer template
├── common-mortakaz/
│   └── mortakaz-discovery.md      ← task + answer template
├── q1-cumin-memory/
│   └── cumin-memory-module.md     ← task + answer template
├── q2-arabic-tts-stt/
│   └── arabic-tts-stt-proposal.md ← task + answer template
├── q3-ghaymah-signup/
│   └── ghaymah-signup.md          ← task + answer template
├── q4-static-site-deploy/
│   ├── static-site-deploy.md      ← task + answer template
│   └── site/
│       ├── index.html
│       └── style.css
└── q5-llm-agent/
    ├── llm-agent.md               ← task + answer template
    ├── agent.py                   ← starter LangChain agent
    └── requirements.txt

How to submit (GitPasha)

Answer every question by editing the template files in place.

Commit as you go, not one big dump at the end:

git add . && git commit -m "q1: memory module design"

git push origin main when done.
Verify on GitPasha that every file (including your Q4 site source and Q5 code) is present.

Scoring rubric

Question	Weight	What “excellent” looks like
common-qabilah	5%	Real profile URL
common-mortakaz	15%	Two concrete products, technically-grounded integration with Ghaymah's AI stack, honest challenges
Q1 memory module	25%	Clear memory taxonomy, concrete storage choices, sensible write/read policy, runnable pseudocode, Ghaymah integration
Q2 TTS/STT	15%	A well-justified model + dataset with correct Arabic specifics (dialect, diacritization, license)
Q3 signup	5%	Confirmed signup + thoughtful platform observations
Q4 static site	15%	A live URL that resolves + source committed + a short write-up
Q5 LangChain agent	20%	A working agent wired to Ghaymah's LLM endpoint, with at least one tool and clear code

Good luck — and welcome to the tribe. 🐪

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