🚀 Strategic AI Career Roadmap (2025–2028+)
🧠 Introduction: Conscious Strategy, Personal Journey
This post details my structured and deliberate professional and intellectual growth strategy in artificial intelligence, integrating my career ambitions, skills, and the opportunities available at Red Hat.
The roadmap has been carefully crafted, balancing theory, practice, and a long-term career vision.
🎯 Strategy Stages
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║ ║ STAGE 0: Foundations (AI Literacy & Mindmap) ║
║ 3–6 mos. ╠════════════════════════════════════════════════╣
║ ║ ✔ AI Engineer 2025 (Udemy) ║
║ ║ ✔ Kai-Fu Lee (AI Superpowers audiobook) ║
║ ║ ✔ Melanie Mitchell (Thinking Humans audiobook) ║
║ ║ ✔ Mind maps, notes, reviews, reflections ║
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║ STAGE 1: Solidifying Fundamentals & Strategic Context ║
║ (AI Awareness+) ║
║ 3–6 mos. ║
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║ ✔ Coursera "AI for Everyone" (Andrew Ng) ║
║ ✔ Red Hat Expert Extra (AI basics & overview) ║
║ ✔ Udemy: "AI for Professionals" (business perspective) ║
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║ 🎓 Berkeley Executive Education ║
║ "Artificial Intelligence: Business Strategies" (3 mos.) ║
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║ STAGE 2: Red Hat Specialization & AI TAM Role ║
║ (AI TAM Specialization) ║
║ ~6–12 mos. ║
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║ ✔ Learning and specialization in RH AI products (OpenShift AI, ║
║ RHEL AI, Granite models, deployments, API, integrations) ║
║ ✔ Gradual transition from OCP TAM to AI TAM ║
║ ✔ Increasing involvement in client AI projects ║
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║ STAGE 3: Technical Foundations & Specialization ║
║ (AI Technical Literacy) ║
║ ~12–18 mos. (parallel, steady pace) ║
║ 🚩 Full AI TAM readiness ║
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║ Coursera ║ Mathematics ║ Python (refresh, projects) ║
║ Andrew Ng ║ (Khan Academy ║ ║
║ ML / Deep ║ or MIT OCW) ║ NumPy, Pandas, Scikit-learn, ║
║ Learning ║ Algebra ║ Jupyter, light ML prototyping ║
║ ║ Statistics ║ ║
║ ║ Probability ║ ║
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║ STAGE 4 (optional, unclear vision – temporary, for review) ║
║ Deep Specialization ║
║ (MIT MicroMasters or similar Data Science level) ║
║ ~12–18 mos. (hard mode, very advanced) ║
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║ ✔ MIT MicroMasters: Statistics and Data Science (or similar) ║
║ ✔ Optional deeper technical exploration (LLMs, GenAI, RL) ║
║ ✔ Top-level strategic capabilities ║
║ (comprehensive AI strategic advisory) ║
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║ STAGE 5 (optional, unclear vision – temporary, flexible) ║
║ Executive AI Leadership ║
║ ~3–6 mos. (flexible for review) ║
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║ ✔ Berkeley Executive Education ║
║ "Artificial Intelligence: Business Strategies" ║
║ ✔ Strategic & executive skills, roadmaps, pre-sales, ║
║ consulting, organizational transformations ║
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🔥 Final Thoughts
This roadmap serves as a structured yet adaptable approach to achieving my personal and professional AI ambitions.