Data Analyst — Looking for a Practical Roadmap to Transition into GenAI and Agentic AI

[u/BabbaYaga_69]

I have around 4 years of experience as a Data Analyst. My current skills include SQL, Python (Pandas, NumPy), basic statistics and machine learning, and dashboarding.
With Generative AI and agentic AI evolving rapidly, I want to upskill strategically and future-proof my career rather than chase every new trend.
I’m exploring career paths such as AI Engineer, AI Solutions Engineer, Analytics Engineer, and Data Engineer, but I’m unsure which direction would be the best fit for my background.
I’d appreciate advice on a few things:
Career path: Which roles should I realistically target with my experience?
Skills: What should I learn, and in what order? GenAI, LLMs, RAG, AI agents, APIs, cloud, software engineering, or deeper ML?
Required depth: How proficient should I be in each skill to become job-ready?
Learning roadmap: What should my 6–12 month plan look like?
Projects: Which 2–3 practical projects would help me build a strong portfolio and land interviews?
Resources: Which courses, YouTube channels, GitHub repositories, documentation, or hands-on platforms should I follow? Free resources would be a bonus.
I’m not looking to become an AI researcher or train models from scratch. My goal is to build practical GenAI applications and agentic systems while leveraging my existing analytics experience.
I’d love to hear from people who have transitioned from analytics into AI-related roles or are currently working in these fields.
If you were in my position, what would you learn first, which role would you target, and what roadmap and resources would you follow?
I’m looking for realistic, actionable advice based on industry experience rather than just a list of trending technologies.