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 underq5-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 mainwhen 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. 🐪