A concise, data-driven snapshot of hiring demand in Egypt for AI/GenAI roles, plus prioritized next learning steps based on observed market frequency.
Important: While 30 listings were analyzed in the broader task, the only frequency data provided in context is for N=23 postings. This report uses N=23 wherever percentages or counts are shown.
The table below summarizes the top 20 most in-demand technical skills/tools observed across the provided postings. Items are grouped to avoid duplication (e.g., “Sklearn” and “scikit-learn” are treated as one).
| Skill / Tool (grouped) | Positions | Share | Category |
|---|---|---|---|
| Python | 16 | 69.6% | Programming |
| LangChain | 8 | 34.8% | LLM framework |
| RAG (Retrieval-Augmented Generation) | 8 | 34.8% | GenAI pattern |
| LLMs (Large Language Models) | 7 | 30.4% | GenAI foundation |
| scikit-learn (Sklearn) | 7 | 30.4% | ML library |
| Machine Learning (ML) | 6 | 26.1% | Core discipline |
| Natural Language Processing (NLP) | 6 | 26.1% | Core discipline |
| TensorFlow | 6 | 26.1% | Deep learning |
| PyTorch | 6 | 26.1% | Deep learning |
| Pandas | 6 | 26.1% | Data tooling |
| CI/CD | 5 | 21.7% | DevOps |
| Docker | 5 | 21.7% | Containers |
| Kubernetes | 5 | 21.7% | Orchestration |
| MLOps | 5 | 21.7% | Production ML |
| Embeddings | 5 | 21.7% | RAG component |
| APIs (general) | 5 | 21.7% | Integration |
| NumPy | 5 | 21.7% | Data tooling |
| FastAPI | 4 | 17.4% | Backend/API |
| SQL | 4 | 17.4% | Data querying |
| Git / GitHub (version control) | 4 | 17.4% | Engineering |
| Cloud Platform | Positions | Share |
|---|---|---|
| AWS | 8 | 34.8% |
| Azure | 8 | 34.8% |
| GCP | 8 | 34.8% |
| Certification | Positions | Share | Notes |
|---|---|---|---|
| Machine Learning (certification/coursework) | 1 | 4.3% | Explicitly mentioned |
| Artificial Intelligence (certification/coursework) | 1 | 4.3% | Explicitly mentioned |
| Deep Learning (certification/coursework) | 1 | 4.3% | Explicitly mentioned |
| Natural Language Processing (certification/coursework) | 1 | 4.3% | Explicitly mentioned |
| Data Analytics (certification/coursework) | 1 | 4.3% | Explicitly mentioned |
| Azure Data Scientist Associate | 1 | 4.3% | Present in postings (additional) |
| Power BI Data Analyst Associate | 1 | 4.3% | Present in postings (additional) |
This section compares the user’s currently stated coverage (as provided in the context) with market needs, and highlights the top 5 skills/tools to learn next based on how frequently they appear in postings.
| # | Skill/Tool | Mention rate | Count |
|---|---|---|---|
| 1 | scikit-learn (Sklearn) | 30.4% | 7/23 |
| 2 | Kubernetes | 21.7% | 5/23 |
| 3 | Embeddings (deeper focus + operationalization) | 21.7% | 5/23 |
| 4 | NumPy | 21.7% | 5/23 |
| 5 | MLOps (broader practices & toolchain) | 21.7% | 5/23 |
Frequency stats are taken directly from the provided market overview (N=23). No additional skills or tools are inferred beyond that dataset.
Method note: Each item is counted at most once per posting (even if repeated within the same listing). Percentages are computed as (positions mentioning item / 23) × 100.