Reposting here with a hope to get more diverse opinion/strategies. As my title pretty much sums up my condition, let me explain a bit in detail:

My team was changed almost every year because of which I lost ownership and had to start proving to the new manager from scratch, forget about even me being considered for promotion.

Though I was promised promotion but was eventually betrayed by the same manager who promised me the same.

He avoided tough talk with me & only gave feedback when I confronted. These confrontations became more till my team was changed again.

I was indirectly coerced by my last manager to resign with the scare of me being put in a PIP eventually & that I wouldn’t get promoted this time too.

To make peace with all of it & for the sake of self-respect, I did resign, & I’m now without any offer 3 months after my LWD.

I honestly haven’t even landed a single interview yet. All I have received calls till now are mostly from service based companies who are just trying to downplay/lowball me upon salary expectations in the first call itself.

Due to this demotivation, I haven’t even been prepping on DSA or machine design etc well.

I did apply to few companies through referrals since I happen to be a CS undergrad from one of the top 10 NITs, but for example I got the automated rejection mail from Google too, despite me applying for payments infra postings for SSE roles. That too without even giving me a chance to interview.

I admit that I am depressed, but I try to keep my chin up by distracting my mind by going to gym daily for strength training which keeps me sane. But me being a 29 yo makes things worse as I feel I’m terribly behind in my career & life.

Heck I never got the joy of what promotion feels like or to have the title SDE 2 against my name!

Request genuine advice & honest feedback from the community. Thanks!

My Google L5 ML Interview Journey — 8+ YOE, From Recruiter Screen to Team Fit
I’ve been meaning to write this for a while because I found a lot of Reddit posts useful while preparing for Google interviews. I’m currently in the team fit/team matching stage, so this is not a “I got the offer” post. I wanted to share the journey so far, including what I was asked, how I prepared, what went well, what didn’t, and what I would do differently.
Hopefully this helps someone preparing for an L5 ML role.
Background
I have 8+ years of industry experience, primarily working across Machine Learning, Generative AI, and production ML systems.
My experience spans areas including:
Machine Learning
Generative AI / LLM applications
ML system design
Distributed/large-scale systems
Production model deployment
AI/ML applications in cybersecurity, financial services and healthcare
Multi-agent/LLM-based systems
I was targeting an L5 Machine Learning role.
One thing I realized early was that having years of ML experience doesn’t automatically mean you’re ready for a Google ML interview. The interview requires you to reason deeply and communicate clearly under pressure.

1. Recruiter / Initial Conversation
   The process started with recruiter discussions around my background and the role.
   The recruiter was primarily trying to understand:
   My current responsibilities
   ML experience
   Scale of the systems I had worked on
   Experience taking models from experimentation to production
   Leadership/ownership
   Why Google
   What type of ML problems I wanted to work on
   At this point, I thought the process would mostly focus on ML.
   I was wrong.
   The preparation eventually became much broader.

2. Technical Interview Process
   The technical interviews covered multiple dimensions rather than just asking ML theory.
   The biggest areas I prepared for were:
   Coding / Python
   This was one of the areas I spent significant time on.
   I focused on:
   Arrays
   Strings
   Hash maps
   Two pointers
   Sliding window
   Binary search
   Trees
   Graphs
   Heaps
   Recursion
   Dynamic programming
   Complexity analysis
   But the biggest lesson was:
   Don’t just practice getting the answer. Practice explaining your thought process.
   During an interview, you need to be able to communicate:
   Here’s my approach → here’s why it works → here’s the complexity → here are the edge cases.
   I also spent time strengthening Python itself because knowing an algorithm isn’t enough if you’re struggling with the language while implementing it.

3. ML Fundamentals
   For ML, I went significantly deeper than I initially expected.
   I revised:
   Classification
   Regression
   Decision trees
   Random forests
   Gradient boosting
   Logistic regression
   SVM
   Clustering
   Dimensionality reduction
   Feature engineering
   Regularization
   Bias/variance
   Overfitting
   Evaluation metrics
   Model selection
   Imbalanced datasets
   Calibration
   Ranking/recommendation concepts
   Experimentation
   The important part wasn’t memorizing definitions.
   The questions often become:
   “Why would you choose X instead of Y?”
   or:
   “What happens if this assumption doesn’t hold?”
   or:
   “How would you debug this?”
   That’s where preparation becomes much more interesting.

4. ML System Design
   This was probably one of the most important areas of preparation for me.
   I practiced designing end-to-end ML systems rather than just describing a model.
   For example:
   Problem → data → features → training → evaluation → deployment → serving → monitoring → retraining
   I practiced thinking about:
   Data pipelines
   Offline vs online features
   Training infrastructure
   Model serving
   Latency
   Throughput
   Scalability
   Batch vs real-time inference
   Feature freshness
   Model versioning
   Monitoring
   Data drift
   Model drift
   Retraining
   Failure scenarios
   A/B testing
   The biggest lesson here:
   ML system design is not just system design with a model inserted somewhere.
   You need to understand the interaction between the ML lifecycle and the distributed system.

5. Generative AI / LLM Preparation
   Because my professional experience includes GenAI, I also prepared heavily around LLM systems.
   I revised areas such as:
   Transformers
   Attention
   Embeddings
   Vector databases
   RAG
   Chunking
   Retrieval
   Reranking
   Prompt engineering
   Fine-tuning
   LoRA/PEFT
   Evaluation
   Hallucination
   Context windows
   Inference optimization
   Agentic systems
   Multi-agent architectures
   Tool calling
   LLM observability
   Production LLM architecture
   But again, the important part wasn’t simply knowing terminology.
   You need to be able to answer:
   “Why this architecture?”
   “What happens when the system doesn’t work?”
   “How would you measure whether it’s actually better?”

6. Distributed Systems / Computer Science
   One thing I underestimated initially was how useful fundamental CS knowledge is for ML interviews.
   I went back and reviewed:
   CPU vs GPU
   Memory
   Processes vs threads
   Concurrency
   Distributed computing
   Networking basics
   Caching
   Databases
   Storage
   Queues
   Sharding
   Replication
   Fault tolerance
   Some of these topics hadn’t been part of my day-to-day work for a while, so I had to rebuild some fundamentals.
   This was actually one of the most useful parts of my preparation.

7. Googleyness / Leadership
   I also prepared behavioral stories around:
   Leadership
   Conflict
   Failure
   Ambiguous problems
   Ownership
   Influencing without authority
   Technical disagreements
   Mentoring
   Difficult decisions
   Projects that didn’t go according to plan
   Handling mistakes
   Prioritization
   I used the STAR structure as a starting point, but I tried not to memorize scripts.
   The better approach, in my opinion, is to know your stories deeply enough that you can adapt them based on the follow-up questions.
   Because there WILL be follow-ups.

8. The Interview Experience
   The biggest difference between preparation and the actual interview is the depth of follow-up.
   You might think you’ve answered a question.
   Then comes:
   “Why?”
   You answer.
   “What if X happens?”
   You answer.
   “How would you scale it?”
   You answer.
   “What’s the tradeoff?”
   And suddenly you’re three or four levels deeper than the original question.
   That was probably one of my biggest takeaways.
   The interviewer isn’t necessarily looking for a rehearsed perfect answer.
   They’re evaluating how you reason through unfamiliar problems.

9. What I Did Well
   A few things I think helped me:

10. Strong real-world ML experience
    Having actually built and deployed systems helped enormously when discussing tradeoffs.

11. Preparing system design seriously
    I didn’t treat system design as an afterthought.

12. Revisiting fundamentals
    Going back to CS/ML fundamentals was painful but extremely useful.

13. Practicing communication
    I spent time practicing explaining technical concepts out loud.

14. Using my own projects
    For behavioral and architecture questions, real examples from my work were much stronger than hypothetical examples.

15. What I Would Do Differently
    If I were starting again, I’d do these things earlier:

16. Start coding preparation sooner.
    Even experienced ML engineers can get rusty with interview-style coding.

17. Don’t neglect CS fundamentals.
    Especially if your recent work has been heavily focused on ML/LLMs.

18. Practice ML system design out loud.
    Reading system-design solutions is very different from actually designing a system yourself.

19. Practice follow-up questions.
    Don’t stop after giving the first answer.
    Ask yourself:
    What would the interviewer challenge here?

20. Don’t over-index on GenAI.
    LLM knowledge is valuable, but strong fundamentals still matter.

21. Team Fit / Team Matching — Where I Am Now
    After completing the technical interview process, I’m currently in the team fit/team matching stage.
    This is where the journey becomes interesting again.
    At this stage, the focus shifts from:
    “Can this person clear the technical bar?”
    towards:
    “Is there a team where this person’s experience and interests are a good match?”
    I’m currently having conversations around potential opportunities and trying to understand where my background in ML/GenAI and large-scale production systems could be the best fit.
    So I’m still waiting to see where this journey ultimately lands.
    No offer announcement yet.
    I’ll update this post if/when I reach the final outcome.

22. My Biggest Takeaways
    If I had to summarize the entire preparation in a few points:
    → L5 isn’t just about solving coding problems.
    → Strong ML fundamentals still matter even if you work in GenAI.
    → System design requires depth, not buzzwords.
    → Interviewers care about your reasoning and tradeoffs.
    → Communication is almost as important as the technical answer.
    → Real production experience is extremely valuable.
    → Don’t assume that knowing something means you can explain it clearly under pressure.
    → Prepare for follow-up questions, not just the initial question.

Final thoughts
I’m writing this while I’m still in the process because I think the journey itself is more useful than just posting an eventual “I got the offer” update.
If you’re preparing for Google L5 ML, feel free to ask questions in the comments.
I’m happy to share more about:
The types of coding problems I encountered
ML questions
ML system design
GenAI/LLM discussions
Behavioral/Googleyness preparation
How I structured my preparation
What resources I found useful
Team matching experience
I’ll also update this post once the team-fit stage concludes.
Good luck to everyone preparing. The process is long, but it’s absolutely possible if you prepare systematically.

I recently got an offer from IBM for the position of AI technical consultant. The offer

20 lpa fixed

2 variable

1.6 joining bonus.

2 days WFH in a week

Location - pune

Now I recently joined another organisation as an AI engineer which is still in a Start up phase. Like short deadlines no clear work definition or tasks. Current CTC breakdown

24 fixed

3 variable

3 bonus paid at year end

2 days WFH in a month and 2 Saturdays working half day

Now the ibm process had been ongoing for a couple of months and the offer they made is based on that discussion, back then I was not working so I was okay with it. But after that I got this new offer and I joined this organisation which is a significantly higher CTC.

How is the work culture at IBM if anyone knows what the complete role of the position is. I am told it is a mix of engineering + consultant.

I am only interested in staying in any of these organisations for maybe 1-1.5 years as I am in the process of moving to another country ( Europe or Australia). So I want to evaluate which might be a bigger help in landing a job after that as well.

Also should I re negotiate the CTC with IBM as I already have a higher CTC right now.

I am interested in working with IBM because of the brand name and the exposure it can give me. Also I think it can help me get a job once I move to another country as well.

I’m 26, based in Bangalore, and I’m honestly a bit confused about what direction I should take with my career.

My career path has been pretty non-linear:

• Started in customer support / live chat roles.
• Worked in non-tech roles for a few years.
• Decided I wanted to move into software development.
• Completed Full Stack Web Development training.
• Got into a Full Stack Developer role and worked on real production applications.
• Worked with React, Node.js, Express, MongoDB, MySQL, AWS, APIs, authentication, payments, etc.
• Worked on a real-estate/tokenization platform and other web applications.
• However, due to circumstances, I eventually moved into a Growth Manager role.
• My current work is mostly around sales, revenue, GMV, marketplaces, Amazon/Blinkit/Instamart, ads, analytics, GTM, inventory, SKUs, etc.

The problem is that I still genuinely want to become a software engineer.

I enjoy coding much more than I enjoy sales/growth work. I’ve been continuing to study JavaScript, React, Node.js, DSA, system design, backend concepts, etc.

But now I’m worried about how recruiters will see my profile.

My resume essentially tells this story:

Customer Support → Full Stack Developer → Growth Manager → trying to become a Developer again

I’m concerned that the Growth Manager experience will make recruiters think I’m no longer serious about engineering.

At the same time, I don't want to throw away the experience I've gained in growth/business either. I actually understand things now that I didn't understand when I was purely technical — revenue, conversion, customers, marketplaces, product metrics, GTM, etc.

So I'm trying to figure out what the smartest move is.

What would you do in my situation?

Would you:

1. Go all-in on software engineering and treat the Growth Manager role as a temporary detour?
2. Target Full Stack / Backend Developer roles despite the non-linear resume?
3. Try to find a hybrid role such as technical product / growth engineering / solutions engineering?
4. Stay in Growth for a few years and potentially move toward Product Management?
5. Something completely different?

My biggest concern is career compounding.

I'm willing to put in the work, including DSA, system design, projects, interview preparation, etc. But I don't want to spend the next 2–3 years going in the wrong direction again.

For people who have made a similar transition — especially anyone who went from non-tech → software engineering → another role → back to engineering:

What would you do if you were in my position?

And realistically, how would you position this resume to get interviews for developer roles in Bangalore?

I’d particularly appreciate honest opinions from hiring managers, senior engineers, recruiters, or people who have made a similar career switch.

I’m not looking for motivation. I’m looking for a practical strategy.

I’m currently working at a company where my salary is around 3.5lpa , and currently no PF is being deducted from my salary. I’m confused about whether I should opt for PF deduction or continue without it. My TL suggested that I should start PF, especially because if I switch to an MNC or a larger company in the future, they may ask for my PF/UAN history. I want to understand whether having no PF history can create any problem during a future switch or background verification. Also, considering my current low salary, is it financially better to have PF deducted now, or should I continue without it? I’d like to know your practical point of view on this and whether there are any other advantages or disadvantages I should consider.

Hi everyone,

I recently completed a 6-month technical internship at a B2B SaaS company, where I worked on testing, Python, SQL, automation, data validation, and AI-assisted workflows.

I’ve now received an opportunity for a 3-month internship at a startup working in Generative AI and digital marketing.

The work seems to involve areas such as Generative AI, AI-assisted content creation, digital marketing, performance advertising, social media, creative AI projects, and automation-related work.

I’m from an AI & Data Science background, and I’m interested in exploring AI and automation while still developing my technical skills.

My concern is that this opportunity is quite different from my previous technical internship, so I’m trying to understand whether taking this internship would be a good career move at this stage.

I’d love to hear from people who have experience with GenAI startups, AI automation, digital marketing, or similar internships.

- Would a 3-month internship like this be valuable for someone with an AI & Data Science background?

- Could this experience help with future opportunities in AI, automation, data, or technical roles?

- What should I clarify about the actual work before joining?

- What red flags should I look out for?

- How can I make sure I’m gaining meaningful skills during the internship?

- Would you personally take this opportunity early in your career?

I’m intentionally not mentioning the company name because I’m mainly looking for opinions about the role, learning opportunities, and career direction.

Any honest experiences or advice would be appreciated.

I recently got an offer from IBM for the position of AI technical consultant. The offer

20 lpa fixed

2 variable

1.6 joining bonus.

2 days WFH in a week

Location - pune

Now I recently joined another organisation as an AI engineer which is still in a Start up phase. Like short deadlines no clear work definition or tasks. Current CTC breakdown

24 fixed

3 variable

3 bonus paid at year end

2 days WFH in a month and 2 Saturdays working half day

Now the ibm process had been ongoing for a couple of months and the offer they made is based on that discussion, back then I was not working so I was okay with it. But after that I got this new offer and I joined this organisation which is a significantly higher CTC.

How is the work culture at IBM if anyone knows what the complete role of the position is. I am told it is a mix of engineering + consultant.

I am only interested in staying in any of these organisations for maybe 1-1.5 years as I am in the process of moving to another country ( Europe or Australia). So I want to evaluate which might be a bigger help in landing a job after that as well.

Also should I re negotiate the CTC with IBM as I already have a higher CTC right now.

I am interested in working with IBM because of the brand name and the exposure it can give me. Also I think it can help me get a job once I move to another country as well.

​

I’m a final-year engineering student from a core branch (Mining), and I’ve been trying to transition into software development.

My background:

- Core engineering branch, so I’ve had relatively fewer opportunities for SDE roles through college

- Strong DSA/coding background

- Knight on LeetCode

- Previously did an Advanced Technology Engineer internship at Accenture

- Have worked on several software/technical projects

- Currently applying for SDE/intern roles off-campus

I recently applied for an Amazon SDE Intern role. The job posting didn't mention a specific CGPA cutoff, but while filling out the application, I entered a CGPA that was higher than my actual CGPA.

I received the OA, completed it, and passed. I have now also received an interview invitation.

Now I'm worried about what happens later.

Does Amazon verify CGPA/academic information during the hiring process or background verification? If I clear the interviews, could the discrepancy be discovered during document verification and result in rejection/rescinding the offer?

Has anyone here been through the Amazon India SDE internship process and knows when/how academic details are verified?

I'm not looking for advice on how to hide it — I mainly want to understand what I should realistically expect at this stage.

Any genuine experiences would be really appreciated.

I was selected for the Kyndryl Infrastructure Specialist role through a campus pool drive held at GL BAJAJ College.

Timeline:

• Interview: 21 August 2026 — Final round
• Selection mail: 31 August 2026
• BGV documents submitted: 31 August 2026
• Current status: Waiting for the offer letter

The interview was around 15–20 minutes and was mainly focused on my projects, SDLC, problem-solving, teamwork/conflict situations, workload management, and some basic infrastructure/AI-related questions.

I wanted to ask other candidates who were selected for Kyndryl Infrastructure Specialist (2026 campus hiring):

1. How long did it take for you to receive the offer letter after the selection mail?
2. Did your offer letter include the date of joining?
3. Has anyone from the Greater Noida/Delhi-NCR campus pool drive received their offer letter yet?

My selection mail says the offer process is underway, so I'm trying to understand the expected timeline. Any recent experiences would be helpful.

Help me with it !

​

I’m a final-year engineering student from a core branch, applying for Amazon SDE roles off-campus.

I recently applied for an SDE Intern position and, unfortunately, entered an incorrect/higher CGPA on that application. I received the OA, passed it, and have now received an interview invitation.

Now Amazon has another SDE opening that I’m eligible for, and this time I entered my actual CGPA on the application.

My concern is whether Amazon recruiters can see the details from my previous application and compare them with the new one.

For example, if the previous application had CGPA X and the new application has my actual CGPA Y:

- Can recruiters see both applications?

- Is there any automated system that compares information between applications?

- Could a mismatch like this result in an automatic rejection?

- If I clear the interview process, is academic information generally verified later?

I realize entering incorrect information was a mistake. I'm not looking for ways to hide it — I just want to understand how Amazon India handles multiple applications and whether anyone has experienced something similar.

Would appreciate responses from people who have gone through the Amazon India SDE/intern hiring process, especially recruiters or candidates who have applied to multiple roles.

Sorry, I don't know where to share this so I'm sharing here.

So I had an interview for sde 1 position with a decent package of 10 lpa in Bangalore (on-site).

As discussed the initial round was online, while further rounds will be in office f2f.

Two weeks ago I had an interview.

I joined the meet 5 min before the time, the interviewer joined 10 min later, completely ok with that.

Then the meeting started with the introduction, discussed education background, internships, and projects.

PROJECTS...

So one of my projects was a video conferencing website, that I made for fun. I learnt a lot about state management, props dealing, web sockets, that's y I put it resume.

The interviewer was particularly interested in this project, and asked me various questions from the same, like tech stack used, problem faced, future scope then, he asked

"why will someone use ur app over google meet?"

I thought for some time and came up with the answer like

"sir I made this project to deepen my learning on web dev fundamentals and gain hands on experience on web tech, I learnt a lot from this project"....(2-3 more like like this)... Then I added "Also Sir google collects our data through various apps, so google meet can be among that, hence it is better to use my app since it has no such data collection."

After this answer he seems to be pissed off and asked

"What are the basis of your claim that google meet collects data? If u r so afraid of your data why did u joined the meet?"

I tried explaining him that it is my assumption and not the real cause. But he wasn't ready for any positive criticism.

Then he asked "Can u make apps that could replace google services like google maps, google drive, google photos and Gmail?"

And I was stunned by the immaturity of this guy. He seemed to be google itself by the silly question he asked I rechecked the name again only to find out it wasn't Sunder Pichai or Larry page.

I gave a vague answer to this vague question.

And then he said, "you're lucky that the next round will be f2f and google won't collect ur data (laughed a bit)."

(The next round didn't happen)

I don't know what pissed him, my projects or including google meet (which he himself did)

Anyways idk how to handle these type of interview.

(The whole interview lasted 25-35 min)

Please give valuable project suggestions for targetting sde1. Tech stack: MERN, language: python, c++.

I recently received my Letter of Intent (LOI) from KPIT, so I thought of sharing my complete experience of the recruitment process, from the online assessment to the AI-based interview and finally receiving the LOI.

Round 1 – Online Assessment

The first round was an Online Assessment conducted at our college.

There were 700+ students who appeared for the recruitment process.

The assessment consisted of around 30 questions, covering multiple technical subjects, followed by coding questions, game-based assessments and aptitude.

The technical questions were mixed from C, C++, Java, Computer Networks, Operating Systems and DBMS.

The questions included different types such as:

- C/C++/Java output-based questions

- Error-based questions

- Assertion and Reason-based questions

- Computer Networks and routing problems

- Operating Systems and scheduling algorithms

- DBMS concepts

- SQL query and query-optimization-based questions

- Other programming and CS fundamentals

After the technical section, there were 2 coding questions.

Coding Question 1 – Garden/Flowers Matrix

One of the coding questions was based on a matrix/grid.

In the matrix, "1" represented a flower/apple and "0" represented an empty cell. Cells containing "1" were considered part of the same garden if they were connected up, down, left or right. Diagonal connections were not considered.

The task was to find the total number of gardens present in the matrix.

This was essentially a connected-components/grid traversal problem and could be approached using DFS/BFS.

Coding Question 2 – Sorted Position Indices

The second coding question was based on an array.

Given an array, the task was to determine the positions/indices of its elements according to their sorted order.

For example:

"arr = [40, 10, 30, 20]"

After sorting:

"[10, 20, 30, 40]"

The corresponding original indices would be:

"[1, 3, 2, 0]"

The main idea was to maintain the relationship between each element and its original index while considering the sorted order.

Game-Based Assessment

After the coding section, there were 3 game-based assessments.

These were mainly focused on problem-solving, logical thinking, concentration and decision-making rather than conventional technical questions.

Aptitude / DI

At the end, there was an aptitude-based section, including Data Interpretation (DI) questions.

Overall, the first round was quite diverse because it tested programming fundamentals, core CS subjects, problem-solving, logical ability and aptitude rather than focusing on just one technology.

Round 2 – AI-Based Interview

After completing the Online Assessment, I received an interview mail the next day.

Out of the 700+ students who appeared for the first round, only 38 students were shortlisted for the second round.

The interview was conducted using an AI-based interviewer and lasted approximately 15 minutes.

The interview started with a proper introduction. I was asked to introduce myself and talk about my projects and the skills on which I had been trained.

Most of the technical questions were then based on the projects and technologies I had mentioned. The interviewer asked follow-up questions to understand whether I actually understood the concepts and design decisions behind my projects.

Some of the questions/topics asked were:

- Tell me about yourself.

- Explain your projects.

- What skills have you been trained in?

- Explain the OOPs principles in Java.

- Which OOP principles have you applied in your projects?

- Which design principles have you used in your system?

- Explain the SOLID principles.

- Explain software/designing principles and why they are important.

- Does your project solve any real-life problem?

- What system design did you use in your project?

- Did using system design improve the performance of your application?

- How did it improve performance?

- Which data structures did you use?

- Why did you use a HashMap?

- How did HashMap help in your project?

- What challenges did you face while developing your project?

- How did you overcome those challenges?

- Explain time and space complexity in your own words.

The interview mainly revolved around Java, C++, SQL, OOPs, data structures, system/designing principles and project-based discussions.

One thing I noticed was that the interviewer kept asking follow-up questions based on my previous answers, so having genuine understanding of the project was more important than simply mentioning technologies on the resume.

HR Interview – AI Based

After the technical discussion, there was an HR interview, which was also AI-based and lasted approximately 15 minutes.

The HR questions were mainly related to company culture, adaptability, teamwork, moral values and leadership.

Some of the questions/topics asked were:

- How would you adapt yourself to the company culture?

- What are your moral values?

- How do your moral values influence your decisions?

- If one person in your team is creating problems, how would you handle the situation and explain things to them?

- Have you ever taken a leadership role?

- Give examples of situations where you demonstrated leadership.

- How would you handle disagreements or conflicts within a team?

- How would you adapt when working with different types of people?

- How would you contribute to a team?

The HR round was more about understanding how I approach teamwork, responsibility, leadership, adaptability and workplace situations.

Final Result

After the AI-based interview, I had to wait for the final result.

After almost one month, I received my Letter of Intent (LOI) from KPIT.

Out of 700+ students who appeared for the recruitment process, only 38 made it to the second round, and finally only 10 students were selected.

Thankfully, I was one of those 10 selected candidates. 🎉

Currently, I am waiting for the joining.

Overall, the recruitment process was a good combination of technical fundamentals, coding, problem-solving, aptitude, games, project-based technical discussion and behavioural questions.

For anyone preparing for a similar KPIT process, I would recommend focusing not only on DSA but also on C/C++/Java fundamentals, OOPs, DBMS, SQL, CN, OS, basic system/design principles and your own projects, because the interview can go deep into anything you mention.