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NxtWave's Podcast with Drishti Wali

Podcast Outline: Building a career in Machine Learning

Host: Avinash Dara, Head of Product, NxtWave Guest:

Drishti Wali⁠

⁠

1.Introduction [5 mins]

Mention the highlights of the podcast (key takeaways)

Introducing the students to Speaker

Setting the stage for conversation

2. Drishti's Journey (5-10 mins)

Dirshti, you’ve had an incredible journey from IIT Kanpur to Cornell, then working at Microsoft, CRED, and now at Ionhealth. Can you walk us through this path? What inspired you at each stage, and was there a moment where you felt, “Yes, ML is the field I want to go deep into?

Did you always plan for a career in Machine Learning, or did it evolve gradually as you explored more options during college?

For students listening, balancing academics, personal interests, and career planning can be hard. What helped you stay consistent or focused?

Speaker Note: You can reflect on any early uncertainty, how you explored opportunities, and how you made choices even when the path wasn’t always clear.

3. The AI Revolution & Real-World Projects (20–25 mins)

We’re in the middle of the AI revolution. Terms like Agentic AI, NLP, automation, and deep learning are everywhere. Can you break these down for our students in simple terms?

Can you please share your thoughts on what has changed in the AI landscape that is enabling deep research to rapidly scale into real-world applications? In your opinion, what’s driving this shift?

In your opinion, what are some recent breakthroughs or applications in AI that genuinely excite you and show us where the field is heading?

To help students connect all these exciting concepts with actual work, can you take us into your own journey with real-world ML systems and the potential of it?

4. Honing skills for ML (10 mins)

For students inspired by all this, where should they begin? Are there specific domains like NLP, GenAI, or agentic systems that are more booming right now?

What are core technical skills, technologies and tools students should focus on if they want to work on cutting-edge AI projects to become job-ready in this space?

Can you also share what kinds of opportunities are available today in this space both in India and abroad? For someone starting out, what are the possible roles they can explore and how should they go about finding or creating those opportunities?

5. ML at scale (10 mins)

You’ve worked on impactful ML projects at both Microsoft and CRED; two very different environments. Can you walk us through one or two projects that helped shape your thinking?

Also at Microsoft where you focused on NLP using Deep AI techniques. Most students may not be familiar with what exactly goes into building large-scale NLP systems like the kind of problems you solve, and how working in NLP is different from ML. Can you break it down for us?

Can you also give us a glimpse into what your day-to-day work as an ML Engineer looks like what kinds of problems you solve, the tools or frameworks you use, and how you approach problem-solving in such a dynamic field?

Speaker Note: Walkthrough of a real ML project from problem to model to deployment and impact. Can explain what NLP projects look like in the industry, how they're different from other ML domains

6. Break the Myth Segment (5-10mins)

Note to speaker: Choose 1–2 of these myths to talk about

“If I don’t know how to use ChatGPT or GenAI tools, I’ll fall behind.”

GenAI is hot right now, and there’s a lot of buzz around tools like ChatGPT, Claude, Midjourney, and more. Many students feel pressured to quickly jump on the trend or build something flashy to feel relevant. Can you talk about how students should balance this hype with building strong foundational skills in ML?

“ML is only for people who are from a CS background.”

Many learners from non-CS branches feel unsure about jumping into ML—can you talk about interdisciplinary roles and how they can still break in?

“If I’m not from IIT or a top college, I can’t get into ML roles.”

How true is that in the real world? What really helps students get noticed today?

7. Advice & Reflections(10 mins)

If you could go back to your college days, what would you do differently?

Can you share one underrated resource or experience that really helped you?

What’s one small habit or mindset that gave you an edge?

Are there any blogs, books, or online resources that helped you early on or still help you today?

How do you personally stay updated with fast-changing trends in AI and ML? Also What would you recommend students do to stay relevant and curious?

Speaker Notes: Recommendations around learning, upskilling, or even daily routines can make a big difference for listeners.

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