This essay as told is based on a conversation with Rahul Kasanagottu, a 32-year-old customer engineer at Google, specializing in AI and machine learning, and based in Austin. His identity and employment have been verified by Business Insider. The following 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 bandwagon came along, I realized I wanted to get into this field.
At pivotal moments in technology, many people get 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 that’s when the AI boom hit. Google offers generous parental leave and I thought it would be a good opportunity to spend some 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 about four to five different positions, and six months ago I moved from senior technical account manager to a Google Cloud customer engineer specializing in AI and machine learning. In this role, I create demos and show customers how to use Google’s AI products.
I continue to improve myself continuously. The product evolves every day. Today I’m working with one type of client, but tomorrow I might be working with a completely different client with completely different needs. The learning curve is continuous.
Here are the 11 books and courses that helped me improve my skills.
Manuals:
Other books:
- “Genesis” by Henry Kissinger, Craig Mundie and Eric Schmidt
Courses and YouTube channels:
Books
The two books I read cover to cover were “Designing Machine Learning Systems” and “Generative AI on AWS”. The latter benefits from a support course in deep learning which was very decisive in my early learning.
Chip Huyen’s two books were my favorites. He explains things in a very accessible way and gave me an understanding of how organizations use and implement AI. At first, it was difficult to understand the difference between the research side and the applied side of AI. These books helped me realize that my interest lay in applied AI.
“Power and Prediction” was another favorite. He explains how technology must evolve economically to make a difference. For example, if the light bulb still cost thousands of dollars, it would not electrify every house today. Books talk about AI in similar terms.
“Genesis” also stood out. He talks about the future of AI and the challenges it will pose.
Andrew Ng’s classes were also very helpful. He is an extraordinary professor and the founder of Google Brain.
Google’s culture also helped. Without the support of my manager and my teammates, I would not have had the time to develop personally. I had to prioritize my work and personal learning while caring for a new daughter. My wife also had to sacrifice a lot of hours so that I could work on my own business.
Solo projects
The books generally do not come with homework, but the lessons have many practical exercises. Over time, I realized it was the missing piece in my CV. It became difficult to convince hiring managers that I could do the job because it required creating demos and completing hands-on projects.
I realized I needed to do my own projects and AI tools were incredibly helpful for that.
For other people who want to transition, I would say keep working hard. It takes you time to connect the dots on complex issues and sometimes you have to read the same thing over and over again to fully understand this concept.
Many people who want to get into AI, myself included, are in a hurry to land an AI job after six months. But many machine learning concepts take time to internalize. Perseverance is therefore necessary.
