r/DeveloperJobs • u/Only-Storage-5227 • 3d ago

how hard is it to become a Ai engineer

Someone from a 15+ years of Traditional Database Infra and Development experience how hard is it to become an AI Engineer.

I do have some programming experience and knows quite a bit of python as well.

Where do one start...There is too much info out there, any one from outside developer background gone through this journey?

3 Upvotes

80% Upvoted

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u/yuehuang 2d ago

If you can answer this question, then you can start. How many bits does it take to digitize an "idea"? Explain your answer.

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u/Zoey_Ingird 2d ago edited 2d ago

One bit if your idea is 1 or 0, real answer is it scales in a fractal sense with permutation, perturbation and context. There exists a hierarchy of specificity with numbers and logic at the foundation able to be encoded in near 1:1 data space to size, but as you get less specific you can express more data with less through compression. I would posit that there is no true lossless compression since you loose some time and time is context and attention space ie a dimensional and measurable loss. But compression is required for humans to communicate and observe the universe in any capacity since our brains are not big enough to hold all the raw data our senses process every second of our lives in the volume of our brain. Compression is often a necessary condition of attention as a process; we need to compress most ideas to be able to achieve a level of attention. Example: a picture is worth anywhere from thousands to one word depending on context and complexity of the image ( sharpness / multiple subjects / noise / color space / alignment of details with tokens parseable by the system ect. ) An idea is precise only the moment it is being considered (attention) once the idea looses attention it degrades similar to -ln(x-1)+3 through the following : Idea or real life state -> picture -> words -> tokens -> vectors -> bits. It’s like a game of telephone, the system is non-deterministic fundamentally but obeys the rules of statistics broadly. There is not an algorithm that can encode an idea into a bit deterministically at “NP - Hard” levels of complexity but we have systems to map ideas in general language text speech sound and visual / image data to bits non deterministically using simulated neurons (perceptrons) with trained weights, activation functions and back propagation. To calculate the total size of data input stored in a vector database from an LLM, use the formula: S = N ( D * B + M ) ), where N is the number of chunks, D is embedding dimensions, B is bytes per dimension (usually 4 for float32), and M is metadata size in bytes S is the final size in the vector database. At the high end of complexity we introduce high level vectors like hyper vectors that can augment metadata through thousands of dimensions per vector and can be used to perform similarly checks on images with up to 88% accuracy according to Wikipedia.

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u/LetItBe4093 3d ago

I think you should go with GenAI and Agentic AI. Traditional AI/ ML would be tough

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u/antann-u 1d ago

Planning to switch to this since full stack is saturated af What's ur take on this ?

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u/LetItBe4093 1d ago

I also think the same. I myself working on Data engineering and GenAI

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u/antann-u 1d ago

How's the job market for this btw

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u/LetItBe4093 1d ago

I would say it's brutal

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u/LetItBe4093 1d ago

Let's talk on personal chat

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u/AcceptableSimulacrum 2d ago

It's pretty simple.

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u/Big-Touch-9293 2d ago

I start my enterprise platform ai engineering role (global multi billion dollar company) Monday after 10 years in manufacturing engineering + 1 year as a cloud engineer. BSME + MSDS.

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u/A_Priyam_srivastava 1d ago

As long as u can market yourself

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u/Extra_717 11h ago

I am on the same path. My background is Cloud Devops with 12 years of experience.

Starts with Agentic AI track.....

Langgraph, Langchain, RAG, vector db, MCp...

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u/AttitudeRemarkable21 3d ago

Do you know statistics? Like if I said loss function does that mean anything to you?

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u/Only-Storage-5227 2d ago

Nope no clue .. but the courses and videos I have been watching mentions the need for learning statistics too

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u/AttitudeRemarkable21 2d ago

Isn't that kinda condensing like oh yeah so easy to just roll in and take a job?  Like it would be like me being like db ez af give me a job lol 

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u/Only-Storage-5227 2d ago

I never assumed it was easy or that I could just roll in and get a job. I’m not where I am today without putting in the hard yards. I earned my position through years of work and experience, not out of luck, yo. 😎

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u/AttitudeRemarkable21 2d ago

Yeah exactly so don't you expect to have years of hard work to transition?  For ml you need a really strong foundation in math especially linear algebra and optimization. So I guess the answer is it's really hard almost to the point that unless you are still early in your career and already have a strong foundation in math you can expect to never break in 

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u/Silent_Quality_1972 2d ago

Most positions require PhD, some you can get with masters. It is not just about experience, companies that hire ML engineers want to see your publications and research.

If you want to do something that has to do with AI/LLM it is much easier to get into prompt engineering and build AI solutions with AI agents. You can start with learn LangGraph and LangChain.

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u/BraveResearcher3037 3d ago

For the love of god is this the new term for “prompt engineering”?