This as-told-to essay is based on a conversation with Rahul Kasanagottu, a 32-year-old customer engineer at Google, specialized in AI and machine learning, and based in Austin. His identity and employment have been verified by Business Insider. The following has has been edited for length and clarity.
For a few years, I worked in a customer success role at Google, but when the generative AI train came along, I realized I wanted to get into that.
During pivotal moments in technology, a lot of people go into it with the goal of making money. I think more people should think about getting into AI to help influence how it will be used by others — and that’s what I wanted to do.
My daughter was born in April 2023, and the AI boom hit right around then. Google offers generous parental leave, and I figured it would be a good opportunity to spend time with my daughter and start reading books about AI.
Paternity leave definitely helped me start the journey, but it took me about two and a half years, 11 books, and hours of watching videos to land a job on an AI team. I interviewed for around four to five different roles, and six months ago, I transitioned from a senior technical account manager to a Google Cloud customer engineer specialized in AI and machine learning. In this role, I build demos and show customers how to use Google’s AI products.
I’m still up-skilling continuously. The product is changing every day. Today I’m working with one kind of customer, but tomorrow I might be working with a totally different customer whose needs are completely different. The learning curve is continuous.
Here are the 11 books and courses that helped me skill up.
Textbooks:
- “AI Engineering” by Chip Huyen
- “Designing Machine Learning Systems” by Chip Huyen
- “Designing Data-Intensive Applications” by Martin Kleppmann
- “Hands-On Large Language Models” by Jay Alammar and Maarten Grootendorst
- “Generative AI on AWS” by Chris Fregly, Shelbee Eigenbrode and Anje Barth
- “Deep Learning” by Ian Goodfellow, Yoshua Bengio, Aaron Courville
Other Books:
- “Competing in the Age of AI” by Marco Iansiti and Karim R. Lakhani
- “Prediction Machines” by Ajay Agrawal, Joshua Gans, and Avi Goldfarb
- “Power and Prediction” by Ajay Agrawal, Joshua Gans, Avi Goldfarb
- “Genesis” by Henry Kissinger, Craig Mundie, and Eric Schmidt
- “Deep Medicine” by Eric Topol
Courses and YouTube Channels:
- Deep Learning Specialization taught by Andrew Ng
- 3Blue1Brown videos on YouTube about visualizing mathematics and ML fundamentals
Books
The two books I read end-to-end were “Designing Machine Learning Systems” and “Generative AI on AWS.” The latter has an accompanying course in deep learning that was very pivotal in my early learning.
The two books by Chip Huyen were my favorite. He explains things in a really approachable way and he opened my understanding of how organizations use and implement AI. At first, it was hard to understand the difference between the research side and the applied side of AI. These books helped me realize my interest lied in applied AI.
“Power and Prediction” was another favorite. It talks about how technology has to scale economically to make a difference. For example, if the electric light bulb still cost thousands of dollars, it wouldn’t electrify every house today. The books talks about AI in similar terms.
“Genesis” also stood out. It talks about the future of AI and the challenges it will pose.
Andrew Ng’s courses were also really helpful. He is an amazing teacher and the founder of Google Brain.
The culture at Google also helped. Without support of my manager and teammates, I wouldn’t have had the time for personal growth. I had to prioritize my job and personal learning while also taking care of a new daughter. My wife also had to sacrifice a lot of hours for me to work on my own stuff.
Solo projects
The books don’t typically come with assignments, but the courses come with a lot of hands-on exercises. Over time, I realized that was the missing piece in my résumé. It was getting difficult to convince hiring managers that I could do the job because it required building demos and doing hands-on projects.
I realized I had to do my own projects and the AI tools were incredibly helpful for that.
For other people who want to transition, I would say you need to keep working hard. It takes time for you to connect the dots on complex problems and sometimes you have to read the same thing again and again to nail that concept entirely.
A lot of people who want to get into AI, including me, are in a rush to land an AI job after six months. But a lot of concepts in machine learning take time to internalize. So persistence is necessary.
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