When I first started coding, I didn’t have a master plan. I was simply experimenting with small apps and websites for fun. But over time, this curiosity became a career that took me to roles at Airbnb, Microsoft, Paytm, and MakeMyTrip, plus a Google Summer of Code win and even co-founding a product startup. Along the way, I’ve mentored 100+ engineers who are now at FAANG companies, unicorn startups, and leadership roles.

Looking back, I can see a few lessons and patterns that helped me grow faster - and also some mistakes that slowed me down. This article is my attempt to give you a practical playbook you can apply right away.

1. Build Projects That Tell a Story

One of the biggest myths in tech is that certifications alone get you hired. In reality, recruiters and hiring managers are drawn to projects that demonstrate impact and ownership.

When I applied for my first major roles, it wasn’t my resume template or certificates that stood out. It was the fact that I had shipped real projects:

• My Google Summer of Code project was adopted by over 80 schools in the US.
• My product startup showed I could think about users, not just code.
• Later, at MakeMyTrip, I improved payment APIs to cut latency by 30%.

Framework for a great project:

1. Problem – What real issue are you solving?
2. Solution – How did you implement it technically?
3. Impact – Did it improve performance, adoption, or save time?

Pro Tip: Don’t waste time building another “to-do list app.” Instead, ask: What problem frustrates me or my friends? Can I solve it with code? That’s the kind of project that tells a story.

2. Master System Design Early

Coding rounds get you into the interview loop. System design gets you the offer.

At Paytm, we once faced a scaling challenge where millions of payment requests were hitting our middleware. If I had only thought about “code correctness,” I’d have failed. Instead, I had to think in terms of scalability, consistency vs availability, and resilience during failures.

Core skills to develop:

• Scalability: How do we handle 10x more users without crashing?
• Trade-offs: Understand CAP theorem and when to prioritize consistency vs availability.
• Resilience: Build for failure - retries, circuit breakers, failover.

How to practice system design:

• Pick common problems (URL shortener, chat app, booking system).
• Dry run your design under stress (what if 1M users log in at once?).
• Get feedback - system design is best learned by discussion, not solo study.

Pro Tip: Don’t just “draw boxes.” Think about flows, failures, and trade-offs.

3. AI Is the New Literacy

Even if your job title doesn’t say “Machine Learning Engineer,” AI will affect your work.

At Microsoft, I worked on Neural Voice and integrated it into iOS and Android apps. That experience taught me that you don’t need to be an AI researcher to contribute to AI products. What matters is understanding the interfaces and integrations: APIs, pipelines, and how AI fits into existing systems.

Ways to get AI-ready:

• Learn to call AI APIs and integrate them into apps.
• Build small projects (resume analyzer, chatbot, recommendation system).
• Stay curious about AI x Domain use cases (fintech AI, travel AI, healthcare AI).

Pro Tip: You don’t need to train models from scratch. Learn how AI enhances products - that’s where the opportunities are.

4. Optimize for Visibility, Not Just Skills

One of the hardest lessons: being good isn’t enough if nobody knows.

I’ve mentored brilliant engineers who struggled because they treated their work like a secret. Contrast that with others who blogged about their projects or shared breakdowns on LinkedIn - they often landed offers before they applied.

Visibility channels that work:

• LinkedIn posts or articles
• Medium / MentorCruise blogs
• GitHub projects with clear READMEs
• Conference talks or webinars

Pro Tip: Share what you learn, even if it feels “basic.” Someone else is always one step behind you and will find it valuable.

5. Growth Is Non-Linear, Embrace It

My career didn’t move in a straight line:

• At MakeMyTrip, I was optimizing payment APIs.
• At Paytm, I was firefighting scalability issues at 2 AM.
• At Microsoft, I built voice tech used by millions.
• At Airbnb, I now work on complex engineering challenges in large-scale systems.

Each role looked different. Some were glamorous, others were grind. But every step added skills that built on the last.

Mindset shift: Careers in tech are not ladders; they’re more like jungle gyms. Don’t panic if your title or company looks “off-track” for a while - ask: What am I learning here that compounds later?

6. Common Mistakes Engineers Make (and How to Avoid Them)

After mentoring 100+ engineers, I see the same mistakes repeat:

1. Over-preparing for coding, under-preparing for design. Balance both.
2. Chasing new frameworks instead of fundamentals. Learn CS basics deeply.
3. Ignoring communication. Great engineers explain clearly.
4. Not asking for mentorship early. Feedback accelerates growth.

Pro Tip: Avoid these traps, and you’ll progress twice as fast.

7. How to Practice Effectively

Knowledge without practice doesn’t stick. Here’s how to structure practice:

• Coding: Use LeetCode/Codeforces, but set a limit (2–3 problems daily). Focus on patterns, not quantity.
• System Design: Do mock interviews with peers. Record yourself explaining - clarity improves with repetition.
• AI Skills: Take one weekend project (chatbot, recommender) and finish it end-to-end.
• Soft Skills: Explain your code/design to a friend who’s not technical. If they get it, you’re clear.

Pro Tip: Consistency beats intensity. A little every day compounds.

Final Action Plan

Breaking into FAANG or excelling in AI-driven roles isn’t about chasing every shiny tool. It’s about a few deliberate steps:

1. Build impactful, story-driven projects.
2. Master system design and trade-offs.
3. Become AI-literate.
4. Make your work visible.
5. Embrace non-linear growth.
6. Avoid common mistakes.
7. Practice with structure and consistency.

Your journey might start with a hobby project - but with the right playbook, it can take you to the world’s biggest tech companies.

If you’re preparing for FAANG interviews, scaling your career, or exploring AI startups, I’d love to help you avoid the mistakes I made and fast-track your success.