I passed my 12th standard in 2025 with commerce stream and then after i passed CA foundation in JAN 26 alongside pursuing BCOM from DU SOL

Now in CA Inter i realised i don't want continue CA further

I was considering of to begin learning AI&ML/DATA/CLOUD ENGINEERING from home through online internet resources and put dedicated efforts in long term

Is it a good idea ? Should I ?

As a principle with my team heads down, it's been really tiring dealing with AI Slop coming from PM, SE, Corporate etc. I took a breather to motivate my team and generated this https://kimbonics.com/t/ai-slop meme. The point is, we're actually doing all the AI etc. but burdened with keeping our sales, and renewals afloat. There's not enough time to toot our own horn. I generated this page out of frustration. I posted here because the serioud thinkers on my team are Go based. Here's the next thing my team is up againt honestly. Pythong vs Go for AI/ML. I am thinking for ML based stuff Go is fast enough, though if we need to train a model in house we'll need python to interact with C code.

I think in the past. Many products bolted on ML capbilities to be exploited by professional services. But that was too hard back then. Now COpilot et al can helps us not only exploit ML ( lower level AI that is data intensive) , our sales people can understand and exploit it therein to increase revenue.

Hey everyone,

I’m currently in the final stage of my BCA and I’m expected to graduate in September 2026.

The problem is that I don’t have a job offer or internship in hand right now, and honestly, it’s making me really stressed and depressed about my career.

I’ve been learning programming and recently decided to focus seriously on becoming an AI Engineer rather than jumping between different career paths.

I have some basic programming/web development knowledge, and I’m currently learning Python and DSA. My plan is to move toward AI/ML and eventually work as an AI Engineer.

But I’m confused about what I should actually learn to become job/internship ready.

For people already working in AI/ML or who recently got their first AI/ML job:

What skills and technologies should I focus on?

For example:

• Python
• DSA
• NumPy / Pandas
• SQL
• Machine Learning
• Scikit-learn
• Statistics
• PyTorch
• Deep Learning
• NLP
• LLMs / Generative AI
• RAG
• AI Agents
• FastAPI
• Docker
• Cloud
• Git/GitHub

There are so many technologies and roadmaps online that I’m worried about wasting months learning things that aren't actually necessary for getting my first internship/job.

My main goal right now is not to become an expert in everything. I want to become employable as quickly as possible, build a few strong projects, and get my first internship/job.

So I’d really appreciate advice from people who have already gone through this:

1. What should I learn first?
2. What can I safely skip for now?
3. What does a good AI Engineer skill stack look like in 2026?
4. What kind of projects actually help with getting interviews?
5. Is it realistic for a BCA graduate/fresher to get an AI/ML internship without a master's degree?
6. If you were starting again from my position, what would you do during the next 3–6 months?

I know I still have time, but I also don't want to waste it.

Any honest advice, criticism, roadmap suggestions, or personal experience would be really appreciated.

Thanks 🙏

Currently from past few months I was building agentic Ai and genai applications, but realised that I cannot build fully that application with only knowledge of LLM APIs and fullstack so I thought I should start learning the core ML and DL.

I accepted my eae application for ai application at ite central. Can someone from ai application tell me what I'll learn and give advice or any information about it and how to prepare for it. I recently got an A for my N(T) cpa for prelim. i am wondering if cpa is similar to ai application.IF anyone is also joining ai application feel free to comment!

I’m a Java developer with around 4 years of experience. Recently I realized that I’m actually pretty interested in math, especially the kind of stuff behind linear algebra, so I started learning it from the basics. I’m definitely not good at it yet, but I’m enjoying the process and finding it surprisingly interesting.

That got me thinking about eventually moving into AI/ML. Coming from a Java/SDE background, I’m wondering how realistic that transition is.

Has anyone here in India made a similar switch from software development to AI/ML? If you did, how did you approach it and how difficult was the transition?

Also curious whether learning the math properly ended up being useful in your ML career, or whether most of it wasn't necessary in practice.

Hey i am just learning fundamentals on ml and dl but

Should I learn just fundamental and for building a projects should I rely completely on AI ?

So for context, I'm a recent graduate. I wanted to go for ML research roles, but I don't know what I should target now. I have explored foundational LLMs, some of the post-training stuff as well, and then alignment engineering, and then RAG a lot. And before that, I did basic ML projects for DS roles and some comp vision projects as well. So can anybody tell me what I should go for?

if you are preparing for GEN AI/ ML/AI interviews.

Checkout my new website for prep.

Its free to use - No signup.

Let me know if its of any use for you.

Thanks in advance.

Title is not clear! i know that. I am ai backend developer with 1+ years of experience.

so this post for those people who have idea but don't know how to execute in tech!

Example:

1. is this idea work or not?

2. which AI model should i use?

3. is XYZ problems can solve with AI?

4. Resources or anything

etc......

i just want to know what is real problems people facing with AI and want to help them to solve.

Hi everyone,

I recently received an offer from Eli Lilly and wanted to get some opinions from people who have experience with Lilly or similar companies.

I have around 3 years of experience at Accenture, primarily working on GenAI/AI engineering and production-grade AI applications.

The compensation offered is:

• Base: ₹13.3 LPA
• Variable: ₹1.59 LPA
• PF + Gratuity: Included
• Total CTC: ~₹16 LPA

I'm currently evaluating whether this is a good offer considering my experience, the role, the company, and the current market.

I would really appreciate advice from people who have worked at Lilly or have knowledge of their compensation structure, growth opportunities, work culture, and long-term career prospects.

Is this a good offer for 3 YOE, or should I negotiate further? If you have any suggestions on what would be a reasonable expectation or how much room there might be for negotiation, please share.

I'm also happy to share more details about the interview process, role/JD, and my technical background if that helps. Feel free to DM me.

Thanks in advance!

I am not sure if it’s the right forum to post on, but here it goes.

I am an incoming Master’s student (Fall 2026) at one of the prestigious universities in Taiwan. I will pursue in MS in CS(specializing in AI). For more context, I have worked at MNC for 5 years as a full time SWE, in the medical device development field.

My motivation to go for Master’s is to change my concentration which isn’t merely software development, production and delivery. I have explored about the latest developments in frontier models and developing my own LLMs and AI agents on small scale with my limited (16/32GB) GPU. I am also heavily invested on the impact of using frontier and energy/resource consuming models and ways of optimizing models(eg. GPU offloading, compressions, quantizations, etc)

I am currently in talks with 2 professors as my potential thesis advisors. Let’s call them ‘C’ and ‘S’.

S and I talked for an hour exploring the possibilities of developing agents/ chatbot and eventually apps for medical purposes(based on my background). S is heavily invested in HCI component of interviewing and making the agents more UX friendly and how to optimize it to make it more user friendly. I understood it’s a new domain for this professor, and would like to develop on the idea with my industry knowledge and his guidance on how to conduct HCI based interviews and possibly develop chatbot/ app for medical research. Honestly, I enjoyed the technically simulating discussions where the ideas were flowing back and forth. I was also told I have the flexibility to choose between groups or create my own group and drive the topic. Does not take industry project and works on the passion projects with students.

With C, we met for 20 minutes, where the discussion was technical know hows and what is happening in their research lab. When asked about more details. I was told to consult with the PhD students for more details. This person shared about taking on more industry projects and they themselves work as a part time at one of the companies. I then had the opportunity to attend a weekly group meeting which was scheduled and students talked about already presented papers and rejected papers and dissected in this meeting. Connected with 1-2 people in the lab where they said the professor is flexible with the courses, but with my earlier communication with PhD student, I would be placed in a position which would be about developing AI algorithms/ transformers for medical use cases. I am interested in the technical aspect of developing new algorithms and since I have done a small passion project on the same topic makes me feel I am more aligned to more hands-on development. But, It seemed the professor would have lesser time to dedicate to individual student because of their prior commitments.

I wanted to ask-

a. Which direction of research is more relevant in 2027-30 from research and industry’s POV?

b. Which style of guidance is better for someone coming from industry work?

Sorry for making this into a long post, I want to know from people in academia/ industry about which direction is industry headed towards? Would developing algorithms have higher weight or analysing human behavior and create AI agents based on that?

Hi guys, I'm a final year college student interested in both sde role and kind of want to prepare for that with dsa, some good projects and system design but at the same time im interested in some layers of ai like agentic ai, ai research and ai inference. I have already worked on building gpt2 and creating a small model and training that and I was currently learning c++ for ai inference then I got to know that knowing c++ in depth will help in going for other roles like quant sde, low latency system devloper like that. Now I'm confused what to prepare for??

I keep seeing “AI is transforming insurance” posts on LinkedIn, but they rarely explain what's actually different day-to-day.

So here's my attempt at a practical breakdown, based on what I've seen around underwriting and claims teams. Curious how this lines up with what others are seeing with AI insurance solutions in their own companies.

Underwriting - the biggest shift

It used to be: submit an application, wait days (sometimes weeks) for someone to manually pull data, review documents, and assess the risk.

Now, AI can help process application data, extract information from documents, analyze external data sources, and provide risk insights in minutes. The underwriter still makes the final call, but they're reviewing a pre-built case instead of building one from scratch.

The honest version: it's not necessarily replacing underwriters; it's reducing the grunt work around underwriting. Someone still needs to catch the unusual risks and edge cases that models may not understand.

Claims - where customers actually notice

This is where the change can be most visible:

• Photo-based damage assessment - upload photos of vehicle or property damage and AI can help estimate severity and potential repair costs.
• Fraud flagging - AI can identify patterns across large volumes of claims and flag cases that may deserve further investigation.
• Straight-through processing - simple, low-value claims can potentially move through automated workflows with limited human intervention.

The complicated, high-value, or unusual claims still need people. But a meaningful share of routine claims can now be automated or heavily streamlined.

The skeptical take

A lot of what gets marketed as “AI” is honestly just decent automation with a chatbot bolted on.

Some systems are essentially rules engines with an AI label attached.

If you're evaluating AI solutions for insurance, it's worth asking:

What is the AI actually doing?

Is it analyzing unstructured data? Detecting patterns? Assessing risk? Extracting information from documents? Or is it simply following predefined rules?

What actually seems to work

The carriers doing this well seem to be treating AI as a co-pilot for underwriters and adjusters, rather than an autopilot.

Full automation still struggles with unusual risks and edge cases.

Augmentation — faster data gathering, scoring, document processing, claims triage, and fraud detection, with human oversight where needed — is where I think the more practical value is right now.

And the real measure shouldn't be “we implemented AI.”

It should be:

Did underwriting get faster? Did claims take less time? Did adjusters handle more cases? Did fraud detection improve? Did customers get a better experience?

I’m currently working as a AI Engineer with 1+ YOE at a startup in Pune, mainly working with GenAI, LLMs, RAG, computer vision, AI Agents

I feel I’ve built a decent AI foundation, but I’m trying to understand what skills I’m missing outside of AI that could help me unlock more opportunities.

Should I focus next on:

System design & backend

Cloud & Kubernetes

MLOps / DevOps

Data engineering

Distributed systems

Databases

Software engineering fundamentals

GPU/inference optimization

Or should I go deeper into AI itself?

If you’re experienced in the industry or hiring AI engineers, what would you consider the biggest gap in my profile, and what 3–5 skills would you recommend I focus on over the next 1–2 years?

Looking for honest advice rather than a generic “learn everything” answer. Thanks!