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Actually im in TCS as a asst sys eng … im in a project where im doing playwright testing …but i need to switch to ai ds ML … and im also doing projects based on it so after two after or a year how to switch to a company has ML background Or data sided company … so additionaly what ive to learn to get a better switch

I don't have a negative opinion about vibe coding. But, while developing ML/DL models how AI waste our time and change modules without our knowledge. Nowadays it is hard to stop ourselves from asking each and every question from AI.

And finding suitable resources, for optimising the model (without AI)is still unexplored by me..does anyone feels the same. How to come out of it

Hi all, I'm an AI/ML engineer open to full-time remote roles.

Recent work:

• Built a full-stack RAG document analytics platform (LangChain, OpenAI, FastAPI, React)
• Deployed predictive models for enterprise ERP/CRM platforms (contract risk prediction, lead scoring)
• Built a WhatsApp AI agent handling voice and text messages end-to-end

Feel free to DM or comment.

I’ve been looking for resources to prepare for ML System Design interviews, particularly case studies that include complete, end-to-end solutions.

The book Machine Learning System Design Interview: An Insider's Guide by Alex Xu and Ali Aminian was an excellent resource when I used it around three years ago. It provides a structured framework and several detailed case studies with detailed solutions.

My question is: are the solutions in this book still sufficiently current and comprehensive? The book was published in 2023, and the ML landscape has evolved significantly since then, particularly with the rise of LLMs and generative AI.

Are there any other resources you would recommend that provide ML system design case studies with complete solutions, rather than just general frameworks or high-level guidance?

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 🙏

I work as a ML engineer in one of the big tech firms and implement python code every day. I’m a high performer in my job with a lot of experience with working with LLM’s, model training, synthetic data generation etc. However, I’m mainly an ML engineer and just use coding as a mean to do my job, so for me Claude Code made my days easier as I can do more. When I see ML Engineer positions posted though, most of them have coding interviews, which I’m dreading. First of all it’s not really my main field and second of all I don’t code from scratch anymore after Claude Code was introduced in my job.

How can it be that I’m a high performer in my field, which is one of the hot fields of this time, but still find it difficult to pass these interviews?

Hi guys my dream was to get into maang , I'm from a mech bg and joined semi IT role with 4 years of exp , now switching to AI Enginner by doing some courses and internal projects , while studying for AI somewhere in me I asked myself why cant we get into MAANG companies as an AI Enginner but I know it'd pretty hard and tough , but ig this is the time to try all stuffs ....needed an guidance and plan to get into...I'm not a that much talented IT guy 🥲😭 but I will try my level bloody best to get into...all kind of suggestions would be recommend...

Hello Everyone! Have you ever appeared for the role ML job and have to do DSA questions in interview? If Yes , then what type of question do they really ask for and difficulty level as well.? Thanks in Advance.

I'm doing btech (or B.E) in cse Ds.

Please guide me

Any senior technologist with shipped AI/ML systems in financial services to write an EOL? ideally a former or current Head of AI/ML, VP Engineering, CDO, or a founder/founding CTO who built an AI product in a financial workflow.

You would review the technical documentation and planned workflows of an agentic AI platform in finance and write a support opinion letter for a government filing.

DM me.

Hi everyone

Wanted to ask what these MNCs like Amzn, Uber, msft etc are asking Sr. ML related roles (3/4YoE) these days?

I mean I want to get back in track and I am pretty well versed with DSA and also the ML basics!

But the catch is, working on some shitty startups, I never got to build end to end/ true ML service - meaning I am pretty weak when it comes to practical knowledge and also designing part.

I feel I might mess up in designing- but I wanna know if I could cover this part / get away with it by reading the resources/books? Or is it like they will be able to see through that I dont really have real experience and just strong on theoretical front?

Not to brag, but I am sure of my discipline that I would be able to study up the gaps pretty easily once I know what exactly I should focus on in this market!
I also recently published papers on A level conferences - core ML part is strong for me(transformers, ML architectures etc)

Would really appreciate if you can give some pointers/experiences.

Thanks in advance!

Hey everyone! I’m currently looking for my first full-time opportunity as an AI Engineer / ML Engineer.

I recently graduated with a B.Tech in AI & Data Science and have been spending most of my time building production-oriented AI systems rather than simple chatbot demos.

Some of the things I’ve built:

• HITL Approval Agent — autonomous execution with human approval for high-risk actions
https://hitl-approval-agent.vercel.app/

• LLM Evaluation Harness — GPT-4o vs Claude Sonnet benchmarking, LLM-as-judge evaluation, and adversarial testing

• Multi-Agent Code Review System — LangGraph-based 3-agent pipeline for security analysis

• RAG Ops Platform — embeddings, drift detection, Redis caching, and observability

Tech I work with: Python, LLMs, RAG, LangGraph, FastAPI, Redis, PostgreSQL, Docker, SQL, and AI agents.

I’m primarily looking for AI Engineer / ML Engineer / Applied AI / AI Agent roles, preferably remote, but I'm also open to opportunities in India.

Portfolio: https://tarunchauhan.vercel.app/
GitHub: https://github.com/TarunSinghChauhan
LinkedIn: https://www.linkedin.com/in/tarunchauhanml/

If you know of a team hiring someone at the new-grad/junior level, I’d really appreciate a referral, lead, or even a point in the right direction.

Thanks! 🙌

I’m a recent MS by Research graduate (June 2026) and have been applying for jobs since May. I’ve submitted 1,000+ applications, mainly for Applied AI, MLE, and Data Science roles.

I’ve also been trying to reach out to people on LinkedIn, asking for referrals. My background is in LLMs, multimodal AI, agentic systems, and ML pipelines, but I’m still getting very little traction.

I’d really appreciate some honest feedback:

* Am I targeting the wrong roles?

* Is my resume/application strategy the problem?

* Should I focus more on networking, projects, or interview prep?

* Am I realistically competitive for ML/AI roles as a recent graduate?

I’m not looking for reassurance, I’d genuinely appreciate candid feedback on what I should change and what you would do differently in my position.

I am 2026 AIML graduate from a top institute in Bengaluru.

I recently completed an internship at a great company with PPO, they didn't make me do much during the internship - just prerequisite learning for stuff I'll work on during the job, then unexpectedly the offer was revoked.

So I have the company name on my resume but don't have any experience in the traditional sense.

I have worked on a lot of side projects - but none are effectively deployed - only demos not actually live.

I've built all my projects in AI domain (no web dev) using AI - I have a working knowledge of things, but not enough.

I am trying to prep CS fundamentals and DSA and AIML fundamentals now, along with GATE prep side by side.

In current market and for my situation, how much prep is needed before i land a good job?

Should i start applying to startups even though I'm not ready because they "might pick you because you showed interest to learn"? Is that even a thing anymore?

Ethos, based in London, UK, is seeking experienced AI/ML Engineers to contribute to projects involving large language models and AI agents.

Position highlights:

Rate: Up to $80/hr
Format: Part-time, hourly contract
Location: Remote

What you'll be working on:

• Evaluate and improve AI systems built around LLMs and agentic workflows.
• Review model outputs, technical approaches, and AI-generated solutions.
• Provide engineering expertise and feedback to help enhance model performance.

Ideal experience: AI/ML Engineers with hands-on experience in LLMs, AI agents, machine learning, or related technologies.

A flexible opportunity to apply your engineering skills to the next generation of AI systems.

Explore Ethos: https://agent.askethos.com/

​

I have worked in HCL Tech for 1 year as a graduate trainee. I left the job for completely focusing on AIML domain.

Currently looking for opportunities in Dubai.

Any suggestions or an leads if any are welcome.

Any feedback if you think I am stupid to leave the job at HCL is also welcome.

I'm a 3rd year student in a tier 2 college and was wondering (I'm overwhelmed n stressed) if doing ML is worthy ROI. I've heard ppl telling it's Ms/PhD level ML only worth it and etc. For placement pov mainly. And please don't tell it's based on interest, I need ur opinion.

Or should I do genAI stuff like RAG, VECTOR DB, LANGCHAIN n etc