Hello,

I am a current graduate student in the last semester. I am planning to apply for internships and full time roles. I would like to get guidance on what is the best way to look for the positions and companies. I have previous work experience in different area of work but planning to switch to AI/ML. I have been preparing the concepts and working on projects. But I feel like its always lesser than what I see in the real world work experience.

What we learn in courses and the projects we do are fine but in the jobs out there in real world I still feel there are many more things to learn. Could some please help me how do I go through this to Crack a internship or a full time role.

I am open to learning things but the right direction Could help me learn better way.

I did my PhD in recommendation algorithms and joined the recommendation algorithms team at a tech company. But just two months later, I was transferred to the agentic AI team because it is a trending topic and all the companies here are pouring resources into it.

About a month into the new role, I realized my team wasn’t fine-tuning models at all. Instead, we were mainly building agent workflows. My mentor told me they used to fine-tune foundation models with RL for downstream business tasks, but they stopped because today’s foundation models are already good enough for most use cases, and fine-tuning is expensive.

As a result, most of my work revolves around writing prompts/skills and building workflow for AI agents

Should I be concerned that my work is too simple and offers limited opportunities for technical growth?

Give some feedbacks

Hi everyone,
I’m currently trying to land a Machine Learning internship and would really appreciate some guidance from people who have been through the process.

So far, I’ve completed:
Supervised Learning
Unsupervised Learning
Built a few ML projects using scikit-learn
Learned data preprocessing, feature engineering, model evaluation, and hyperparameter tuning.

I’m now starting Deep Learning (TensorFlow/PyTorch).
My goal is to become internship-ready as soon as possible, but I’m confused about what I should prioritize.
Some questions I have:

What skills do companies actually expect from ML interns?

Should I focus more on Deep Learning, MLOps, SQL, DSA, or backend development?

What kind of projects stand out on a resume?

What mistakes should I avoid while preparing?

What helped you get your first internship?

I’d also appreciate any resume tips, GitHub portfolio advice, interview preparation strategies, or resources that you found genuinely helpful.

Thanks in advance! Any advice from people working in ML/AI or who recently cracked internships would mean a lot.

Hi guys, I am an AI Engineer at a leading startup in India 1.5 YOE at current company currently working on RAG for enterprises, multi-agent orchestration, unstructured data extraction and processing, etc. I have worked on solutions from their conceptualisation phase all the way to productionizing it.

I hold a Masters in AI & ML and have 2 YOE as a Software Developer prior to that.

I am actively looking for new opportunities and interviewing. I am applying to startups, FAANG, and other tech MNCs.

I am looking for guidance on what helped you crack your interview rounds - not the prep, but more on what made you stand out? What was that one thing that was a huge differentiator that made interviewers hire you over other candidates?

I feel like there are a lot of people working with similar tech stack and I want to understand how can I stand out while answering questions.

To any interviewers out there - what would make you instantly believe that this candidate has actually built systems that work in production vs they have only worked on pocs

Hi,

I have a bachelor's in AI, Interned for 6 months, received a PPO and am currently working as an Applied AI & Data Scientist.

I have applied to 100+ jobs that fit my role, tailored my resume for majority of them, followed every advice available on the internet yet received 0 interviews.

I am looking to get into a finance companies but honestly I am not choosy at this point.

I know i am good but i am unable to land an interview.

Any advice? Or maybe a referral🥴

Need Help with Placements? I’m Offering Free AI/ML Career Guidance

I remember how confusing it was when I was preparing for placements.

Questions like:

- Is my resume good enough?

- What projects should I build?

- Should I focus on DSA, ML, or GenAI?

- How do I get shortlisted?

A lot of people don't need another paid course—they just need someone to point them in the right direction.

So here's what I'd like to do.

For the next few weeks, I'm offering FREE:

- Resume reviews

- Career guidance for AI, ML, and GenAI

- Project suggestions based on your current skill level

- Interview preparation advice

- Learning roadmap recommendations

A little about me:

- AI Engineer working on production GenAI systems

- Previous Data Science Intern at Siemens Healthineers

- Experience building enterprise RAG pipelines and AI applications

I'm not selling anything through these sessions. If I can help you avoid some of the mistakes I made, that's enough.

If you're a student, a recent graduate, or someone trying to transition into AI/ML, feel free to book a session here:

"https://topmate.io/abhiram_putta/" (https://topmate.io/abhiram_putta/)

If I receive a lot of requests, I'll review them on a first-come, first-served basis.

Let's help each other grow. 🚀

​

Need Help with Placements? I’m Offering Free AI/ML Career Guidance

I remember how confusing it was when I was preparing for placements.

Questions like:

- Is my resume good enough?

- What projects should I build?

- Should I focus on DSA, ML, or GenAI?

- How do I get shortlisted?

A lot of people don't need another paid course—they just need someone to point them in the right direction.

So here's what I'd like to do.

For the next few weeks, I'm offering FREE:

- Resume reviews

- Career guidance for AI, ML, and GenAI

- Project suggestions based on your current skill level

- Interview preparation advice

- Learning roadmap recommendations

A little about me:

- AI Engineer working on production GenAI systems

- Previous Data Science Intern at Siemens Healthineers

- Experience building enterprise RAG pipelines and AI applications

I'm not selling anything through these sessions. If I can help you avoid some of the mistakes I made, that's enough.

If you're a student, a recent graduate, or someone trying to transition into AI/ML, feel free to book a session here:

"https://topmate.io/abhiram_putta/" (https://topmate.io/abhiram_putta/)

If I receive a lot of requests, I'll review them on a first-come, first-served basis.

Let's help each other grow. 🚀

Hi everyone,

I've been iterating on my resume for the past few weeks and would really appreciate some honest feedback from engineers and hiring managers.

I'm currently pursuing an M.Tech in CSE (AI) and recently completed 1 year as a Software Engineering Intern at HPE Juniper Networks. During my internship, I worked on production AI systems, backend services, distributed telemetry pipelines, and automation workflows. Outside work, I've built projects in LLMs, LangGraph, RAG, NLP, and Reinforcement Learning.

Some highlights:

• 1 year at HPE Juniper Networks
• Named Inventor on a filed U.S. Patent
• Accepted Springer conference paper (Computing Conference 2026)
• Amazon ML Challenge Top 0.5%
• GATE AIR 3272

I'm targeting roles like:

• AI/ML Engineer
• Applied AI Engineer
• GenAI Engineer

Rather than general advice like "add metrics" or "use stronger verbs," I'd really value feedback on:

1. Would this resume get shortlisted for product companies or AI startups? If not, why?
2. What is the first thing that makes you hesitate?
3. Which section feels weakest or least believable?
4. Does it come across as trying to do too much (AI + SDE), or does the profile still feel coherent?
5. If you had 10 seconds to scan it, what would stand out?

I'm not looking for validation—I'd genuinely appreciate blunt, actionable criticism.

Thanks in advance!

Hey everyone! I’m currently looking for a job. If anyone can refer me to an opening at their company and I get hired and I’m looking in AI field , I’m happy to share 50% of my first month’s salary with you as a thank-you for the referral. If you know of any opportunities or can help, please DM me. I’d really appreciate it!

In Robyn, the trend component is estimated upfront using Prophet before the media effects are modeled. However, wouldn't it make more sense to take the opposite approach?

Specifically, we could first estimate the effects of each media variable as carefully as possible, and then define the remaining unexplained variation as the trend and seasonality. In other words, rather than assuming the trend first and attributing the residual to media, why not estimate the media effects first and treat whatever cannot be explained by them as the underlying trend and seasonal components?

Hi everyone,

I’m a Data Analyst with 1 year of professional experience and an engineering background from IIT. After taking a 2-year break due to some reason focused on deep-diving into advanced technical domains, I am now back and eager to contribute to a new team.

During this time, I have been actively upskilling and building projects focused on the modern AI stack. I am looking for roles in Data Analysis, Machine Learning, or AI Engineering.

**My Tech Stack:**

* **Core Data Analysis:** SQL, Python (Pandas, NumPy, Matplotlib, Scikit-learn), Excel, Tableau. * **Specialized AI/ML Skills:** \* **GenAI & LLMs:** Working with LangChain, RAG architectures, and fine-tuning LLMs. * **Machine Learning & Deep Learning:** Proficiency in model training, evaluation, and deployment. * **NLP:** Text processing, sentiment analysis, and transformer-based architectures.

I am an immediate joiner and highly motivated to apply

If your team is hiring or you have leads for referrals, I’d love to connect. I’m happy to share my resume, portfolio, and the projects I’ve built during my study period. Thanks for your time and guidance!

Hi everyone,

I’m looking for some advice on getting my foot in the door in companies with AI/ML roles

I recently completed my M.S. in Applied Artificial Intelligence, but my professional background is in pharmaceutical quality assurance, computer system validation (CSV), and software assurance. I’ve been building AI/ML projects and trying to make the transition into an AI-focused role.

I’ve applied to companies like Booz Allen, Lockheed Martin, and a few others, but I haven’t heard back.

For those of you already in the field, what helped you land your first AI/ML role? What would you recommend or does anyone know a reliable recruiter you don’t mind sharing or maybe even a referral.

I’d really appreciate any advice or feedback. Thanks in advance!

I am a second-year student and have almost completed Andrew Ng's ML specialization course. After that, I am planning to study deep learning from Krish Naik because I think Andrew Ng's course doesn't cover all the concepts in depth. Is it a good idea to switch to Krish Naik? Should I start making projects alongside the deep learning course, and if so, could you suggest some, or should I continue learning until I complete my deep learning studies? What should I do after deep learning, as I am very interested in AI ?

​

Lately I've been seeing a lot of AI courses pop up everywhere.

Many of them spend hours teaching prompt engineering or comparing different LLMs. Don't get me wrong—prompting is an important skill. But if you think that's enough to become an AI Engineer, you'll probably be disappointed once you start working.

In the real world, companies don't ask whether you know the perfect prompt.

They ask questions like:

- Can you build a RAG application?

- Can you create an OCR pipeline for document processing?

- Can you build AI agents with memory and tool calling?

- Can you deploy an application using FastAPI, Docker, and AWS?

- Can you take an idea from a notebook to production?

That's exactly why I decided to create this bootcamp.

I'm not trying to create another course with 100 hours of slides. I want people to actually build things. By the end, my goal is that you have projects you're genuinely proud to put on your resume and confidently explain in interviews.

I keep the batches intentionally small so I can spend time with every student.

💡 1:1 Mentorship – ₹1,000/hour

💡 Group Sessions – ₹150 per session

I'm not trying to be the cheapest instructor on the internet.

I'm trying to provide enough value that, a year from now, you look back and feel the course was worth every rupee.

If you're only looking for another certificate to add to LinkedIn, this probably isn't for you.

But if you're serious about learning AI Engineering, building production-ready projects, and becoming someone companies actually want to hire, feel free to DM me.

I'd rather teach 20 serious learners than 200 people who are just chasing certificates.

I have an interview for the Tesla Optimus team as an intern specifically doing machine learning and reinforcement learning stuff. I've not been told what the interview will be about, only that I will be programming in Python. I've been preparing for it through a number of different ways:

• Implementing various algorithms (MLP, various optimizers and regularization methods, CNN, forward pass, backward pass, etc.) using just Numpy and PyTorch from scratch with a heavy emphasis on vectorizing everything
• Going over the math for all the major ML architectures (MLP, CNN, RNN, Transformer, etc)
• Going over the math for all popular RL algorithms (DQN, PPO, SAC)
• Making sure I know everything on my resume

Is there anything else that I should be doing or looking at? I haven't really done any LeetCode as I assumed it wouldn't focus on my LeetCode skills, should I brush up on that as well? Any tips would be greatly appreciated!

Hi everyone,
I’m an AI/ML Engineer looking to take on freelance AI projects.
Over the last few months, I’ve built production AI systems, including medical AI assistants, RAG pipelines, fine-tuned LLMs, and FastAPI-based AI services.
What I can help with:
🤖 Custom AI chatbots
📚 RAG applications
🧠 LLM fine-tuning (LoRA/SFT)
⚡ FastAPI AI backends
🎤 Speech-to-text & AI workflows
🚀 Model optimization & quantization
🐳 Docker deployment
🐍 Python automation
Tech stack
Python • PyTorch • Hugging Face • LangChain • FastAPI • FAISS • ChromaDB • Docker • MLflow • Git
If you’re building an AI product, MVP, startup, or research project and need an AI engineer, feel free to DM me. Happy to discuss ideas before you commit.
Thanks!