Everything the job description asks for, compared with what you already have, turned into a 12-month plan at about 10 hours a week. Tick items off as you finish them; your progress is saved in this browser.
This role asks for 5 years of AI/ML work and 2–3 years leading AI teams. Studying alone can't produce those years, so you won't meet that bar in 2027. What you can do in 12 months is become a strong candidate for MLOps, AI Infrastructure or AI Platform Engineer roles. Those jobs need exactly your Linux, Docker, GitLab CI/CD, storage, security and 24x7 production background, plus the AI skills below. Two to three years in one of those roles, leading people, puts an AI Team Lead job like this one within reach.
Every requirement from the job description, checked against your CV.
| Requirement | Status | What you have / need |
|---|---|---|
| 5+ years in AI, ML or data science | Gap | None yet. Start building real AI work at NEOM (log analytics, anomaly detection) so it counts on your CV. |
| 2–3 years leading AI teams | Gap | You have led a team of system engineers. That leadership transfers, but not as AI leadership. |
| Master's in CS, AI, Data Science or related | Partial | M.S. Information Technology. Usually accepted as "related field"; certifications strengthen it. |
| Python and ML/DL frameworks (PyTorch, TensorFlow, scikit-learn) | Gap | You script in Bash, awk and sed. Python is the first thing to learn. |
| ML, deep learning, NLP, data science | Gap | Phases 2 and 3. |
| LLMs, RAG, AI agents, vector databases, prompt engineering | Gap | Phase 4. Highest market demand right now. |
| Hugging Face, LangChain, LlamaIndex | Gap | Phase 4. |
| REST APIs and microservices | Partial | You support WebSphere, Java and .NET infrastructure. Need to build APIs yourself (FastAPI). |
| Docker, CI/CD, Git | Have | GitLab cluster (UAT and production), Docker, Red Hat Certified Specialist in Containers. |
| Kubernetes | Partial | OpenShift/Kubernetes in progress. Finish it in Phase 5. |
| Cloud AI deployment (OCI preferred, Azure, AWS, GCP) | Gap | Strong on-premise background; no cloud yet. OCI certifications in Phase 5. |
| MLOps and LLMOps | Gap | Phase 5. Your best entry point: it is operations work applied to models. |
| Project management (planning, estimation, risk) | Partial | PMP training (2008), vendor and SLA management, migration projects. |
| Communicating with business and technical stakeholders | Have | 20 years with banks, hospitals, vendors and end users. |
| English, onsite in Saudi Arabia | Have | Already based in Jeddah. |
| Nice: government, healthcare, finance, regulated sectors | Have | NEOM Hospital, Saudi National Bank, Saudi Post. |
| Nice: security, privacy, compliance | Have | Hardening, quarterly IT audits, endpoint protection. Add AI-specific governance in Phase 6. |
| Nice: GPU training and inference optimisation | Partial | You know server hardware deeply; learn NVIDIA GPU drivers, CUDA basics and inference servers. |
| Nice: computer vision, OCR, speech, multimodal | Gap | Optional track after Phase 3. |
| Nice: presales and proposals | Partial | Customer-facing work at Mushko and HPE. Practise AI proposals in Phase 6. |
| Nice: Arabic | Check | An advantage if you have any. |
The phases build on each other, so take them in order. Each one ends with a portfolio project you can put on GitHub and your CV.
Check each exam's current version and price on the vendor site before booking; codes change every year.
Course videos and reading. One topic per evening, notes in a single document.
Hands-on: notebooks, the phase project, pushing code to GitHub. Building beats watching.
Look for one small AI use case at NEOM, such as anomaly detection on server metrics or a RAG assistant over runbooks. Real workplace AI is what turns this from learning into experience.