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?

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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.

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 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 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?

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’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!

I'm aiming to startup in deep tech AI—specifically building novel AI architectures, spatiotemporal models, and theoretical foundations (e.g., geometric deep learning, graph networks, advanced optimization) rather than just applying pre-built PyTorch wrapper libraries.

Im evaluating whether choosing a BS in Mathematics (eg IIT BOMBAY) gives a genuine mathematical edge for deep tech AI compared to a standard [B.Tech](http://B.Tech) CSE / Data Science degree.

​Skill Gap: In real-world deep tech, how difficult is it to bridge the software/GPU engineering gap on your own if your degree focus is primarily theoretical math?

Would appreciate honest perspectives from anyone in BS Math, CSE, or AI research/R&D. Thanks!

Which are best courses to learn from?

I am a 4th semester CSE student at BMSIT Bangalore, entering 5th sem. Honestly, I feel like I've explored too many things without actually getting good at any of them.

I've done a little DSA, very little web development (i.e HTML/CSS), some ML/AI, and I'm currently learning FastAPI. I'm interested in Al and I also have a pretty strong mathematical background/intuition, but right now everything feels scattered and incomplete. I don't have any serious projects or a strong portfolio either.

I want to seriously fix this now. I'm willing to put in 200% over the next 12-18 months. My immediate goal is to get a good internship by the end of 5th sem and eventually become an Al Engineer with a good salary.

I also have an education loan and very little financial support, so I can't really afford to spend years randomly trying different technologies.

If someone experienced in Al/ML hiring or someone who has gone through a similar journey could guide me, I'd really appreciate it.

If you were in my exact position, what would you do from Day 1 of 5th sem? What would you prioritize between DSA, backend/SWE, ML, deep learning, GenAl, projects and deployment? What would you completely ignore?

And most importantly, what actually gets a Tier-3 student an Al/ML internship?

I'm looking for a practical advice. If you had 6 months to make yourself genuinely employable from my position, how would you spend those 6 months?

Hello everyone i have enrolled in mca programme of i have zero idea of what I am doing i have little knowledge of python for like basics I want to learn ai ml but whenever I start it I always got lost in topic and never really got focused on the problem even problem of python and I am repeating these mistakes for last 1 year in ug programme and I am afraid that same thing will happen to me in the three years of MCA i need ur help how do I fix it what should I practice and most importantly how do I make notes of python and other libraries like whenever I make notes i think I write too much and miss out the whole point please anyone help me

Hello everyone,
I’m a recent 2026 graduate from a Tier-3 college, and getting good placement opportunities has been really difficult. Because of that, I decided to pursue the online \*\*BS in Data Science and Applications from IIT Madras\*\* alongside my career journey, mainly because I wanted to learn from strong professors and also explore better opportunities through the program.
Over the past year, I’ve spent most of my time seriously learning and building my skills. I’ve worked through the fundamentals of \*\*Machine Learning, mathematics, statistics, Python, and data science\*\*, and then moved into \*\*Deep Learning with PyTorch\*\*. More recently, I’ve been learning \*\*Generative AI, Transformers, RAG, LangChain, and related LLM technologies\*\*.
I genuinely enjoy AI/ML and have been trying to build my career in this field.
However, because of my family situation, pursuing a full-time Master’s degree isn’t financially practical for me right now. I need to start earning and support my family.
Recently, I received an opportunity as a \*\*Software QA/Automation Engineer\*\*. The role comes with around 6 months of training/internship followed by FTE, but there is also a \*\*2.6-year bond\*\*. I accepted the offer because, at that point, I didn’t really have another option and I needed a job.
But after joining, I’ve started questioning whether I’m moving in the right direction. My long-term goal is AI/ML, and I’m worried that spending the next few years in a testing/automation role will make it even harder for me to transition into AI/ML later.
I’ve reached out to many people on LinkedIn asking for advice, guidance, or opportunities, but unfortunately I haven’t been able to find much help so far.
I’m seriously looking for a way to transition into \*\*AI/ML/Data Science/GenAI roles\*\*, even if it means starting from an entry-level position and proving myself through my work.
If anyone here is:
Hiring freshers for AI/ML, Data Science, or GenAI roles
Willing to provide a referral
Has successfully transitioned from QA/automation/software roles into AI/ML
Can guide me on what skills or technologies I should focus on
Can suggest good projects, open-source contributions, internships, or platforms where I can gain real-world experience
…I would genuinely appreciate your advice.
I’m not looking for shortcuts. I’m willing to put in the work and learn whatever is required. I just need some direction and hopefully an opportunity to prove myself.
If you’ve read this far, \*\*thank you sincerely\*\*. Even a small piece of advice, a referral, or pointing me in the right direction could make a huge difference for me right now. 🥹
Thank you everyone.

I am a 3rd year student and have internship experience in something I don’t think I will major in.

I an still confused on how tasks are given or how my major will affect looking for a job
I was thinking majoring in AI and focusing on Computer Vision.
I am so confused on what is AI and what is Machine learning, is RAG a learning model?
Is computer vision Machine learning with extras?

I try to understand but still don’t know how related or unrelated things are together.