A Google engineer whose job is changing due to AI explains how she's learning without burning out

This as-told-to essay is based on a conversation with Pratiksha Patnaik, a 30-year-old cloud infrastructure engineer at Google Cloud Consulting, based in Seattle. Her identity and employment have been verified by Business Insider. The following has been edited for length and clarity.

I’ve been with Google for around three years and I started as an infrastructure engineer. I’m still an infrastructure engineer and on a day-to-day basis, I work with customers to build different solutions, depending on the needs of the customer.

At first, I was mostly involved with networking security and infrastructure customers. But as we saw the AI wave come in, we started focusing more on customers that want to adopt gen AI products and solutions.

I didn’t transition into an AI rolebut I’m working with a lot of AI services, and AI engineers who are working on features for those services. My job is a combination of working with customers and the product team, to provide technical solutions for customers. It’s a constant feedback loop to figure out if the solution we’re building is right for the customer we’re working for.

Our job is to know how these products work. Sometimes when we work on the products, we identify feature gaps or bugs, so we need to work with the product team or engineering team.

I’ve been in the same role the whole time, but the nature of my job is changing because of everything going on in the AI space. We get a lot of demand in AI products and we have to do a lot of trainings on it to deliver.

I spend an hour or two weekly on trainings

The more AI progresses, the more difficult it’s become to keep up. As the rate of AI innovation gets quicker, the role of engineers has transitioned from mastery to continuous adaptation at scale.

Just being aware of everything that’s happening in the tech industry, along with what we have to do with the customer, has changed dramatically from what it was like a year ago. Back then, we had to execute within known constraints. But as time passes and AI evolves rapidly, those perimeters have dissolved and we have to invest much more time to learning about changes in this space. We now have to navigate an ever-expanding problem space alongside our customers.

I spend around one to two hours a week up-skilling on new concepts. We have a lot of internal trainings that we can utilize. So I see if there is something new that I’m interested in learning about and that can help me do my job.

I am gaining a deeper level of understanding in high-performance computing, AI observability, model performance benchmarking, and the underlying architecture of GPUs and TPUs.

It can get overwhelming

The culture at Google is very much about constantly learning. Every day we learn about a new tool or model version. That motivates me to keep learning. We also have to skill up in order to put our best foot forward in front of the customers.

But with the pace of technology nowadays, I feel like I need to know everything — and if I don’t learn, I might be left behind.

The reality is that it’s not practically possible to know everything with the changes that are coming out at an exponential rate. To remain effective without burning out, I prioritize intentional depth over exhaustive consumption. By focusing on what really interests me, I can make sure that my learning is not just a chore of “keeping up,” but an investment in expertise.

When I read too much, I get overwhelmed and it’s not possible to retain all of the information I’m consuming. We’re at a point where the amount of information we have is huge and we have to figure out where to spend our time and what’s the most beneficial for us.

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