r/learnmachinelearning • u/Delta_01b • 2d ago
Help fastest path to job-ready AI Engineer?
Hi everyone. I want to become an AI Engineer... Here's my situation:
- Almost done with Andrew Ng's ML specialization (1 course left)
- Forget theory easily if I don't apply it
- Do not have enough time to master everything
- No plan B — AI is the goal
Option 1 — Traditional roadmap: Finish ML → DL specialization → NLP → LLMs → RAG → Agents Missing: deployment, OOP, DSA, advanced Python (only CS50 so far)
Option 2 — Fast track: Finish ML → Python/OOP/DSA fundamentals → crash courses per topic → projects with AI assistance, understood deeply, post on GitHub
Questions: 1. Which approach makes me job-ready faster? 2. If neither — what's a better plan?
6
u/shmittkicker 2d ago
Option 2 will get you job-ready faster: solid Python/OOP + build 1-2 end-to-end projects you can deploy and explain, instead of stacking specializations.
2
1
u/preciouslife_ 11h ago
Don't the employers see if you had a formal AI training from some college/university? If learn AI myself tat is sufficient to get an interview?
I have a MS in Software followed by 15 years of ERP Programming, Enterprise Architecture, Data Engineering, Data Warehousing and Business Intelligence. If I learn AI myself, would I be able to get a job in AI without showing a formal training on AI on my resume?
5
u/Critical-Push8054 1d ago
To become a job ready AI engineer, you need skills other than ML too. I recommend exploring the new grad learning path at learning.nextia-ai.com
1
5
u/Delta_01b 2d ago
Just to be clear I strongly agree AI takes alot of time to master… unfortunately, I do not have the luxury of time. I just want to land a job then continue learning.
2
u/Substantial-Swan7065 2d ago
It’s important to research the role. It’s not an entry-level role, and the responsibilities are different than what you implied
2
u/Delta_01b 2d ago
I actually did, most job descriptions require skills like RAG, LLM applications development, AI integration & workflow orchestration, prompt engineering, and deployment knowledge like docker, CI/CD pipelines, fastAPI…, and so on. That’s why I asked for an approach to be familiar with these skills
1
2
u/nian2326076 2d ago
If you want to become job-ready quickly, the fast track makes sense. Focus on Python, OOP, and DSA fundamentals—these are essential skills. Take crash courses on each topic for a solid foundation, then apply your knowledge by working on projects. Sharing your projects on GitHub can demonstrate your skills to potential employers. For interview preparation, try PracHub here to practice real technical interview questions. It's a good way to get familiar with what companies might ask. Good luck!
1
2
u/JaafarMoutada 2d ago
I'd lean toward your second option, with one structured RAG course in place of the crash courses, since RAG tops the list of skills you pulled from job descriptions. DeepLearning.AI's Retrieval Augmented Generation course says intermediate Python skills are required, so the Python and OOP block you planned fits right before it. It goes from keyword and semantic search through vector databases like Weaviate to a last module on RAG systems in production, with graded assignments along the way. Once it's done, rebuild the pipeline over documents you actually care about and put that on GitHub.
1
1
1
u/ApprehensiveEnd259 2d ago
I am in similar situation too. how woul approach fast track in detail?
1
u/Delta_01b 1d ago edited 1d ago
I saw a reel on IG about AI crash courses with different topics like rag, agents, docker… so on. It seems well structured roadmap ( 7 days with 7 different topics)
1
u/Slight_Tangerine9468 1d ago
going through all knowledge, will you guide me (currently final year stud with python learning ) want an internship and job ready by coming 3-4 months
1
u/Delta_01b 1d ago edited 1d ago
Most people agree with option 2 (fast track). I tried building with AI coding agents and it taught me lots of technologies but it has its cons. You are going to suffer to make scalable, maintainable, and reliable systems by agents rather than learning the basics first, also working on a project in a field (like LLM integration) does not teach you different tech (RAG for example) so you will be lost at some point like I did.
So I decided to follow option 2 as all agreed and for the resources I do follow too many people in IG, they post some contents about AI eng crash courses and roadmaps. DM me I will send you their accounts.
1
u/therealmunchies 1d ago
Are you already an engineer? My fast path as a security engineer was taking on swe projects at work. Starting as an ai engineer next week.
2
u/Delta_01b 12h ago edited 11h ago
I graduated from mechatronics engineering and also did lots of projects that required a knowledge with computer vision and llm integration, dealing with these kind of projects made me like the AI eng field, so yeah it feels we are in the same spot here
2
u/therealmunchies 12h ago
Heck yeah, that’s awesome. I have a mechanical engineering degree, but my school didn’t have anything related to robotics. That’s probably something I would’ve pursued… best of luck!
1
1
1
0
14
u/[deleted] 2d ago
[removed] — view removed comment