This essay is based on a conversation with Pratiksha Patnaik, 30, a Seattle-based cloud infrastructure engineer at Google Cloud Consulting. Her identity and employment have been confirmed by Business Insider. The following has been edited for length and clarity.
I’ve been working at Google for about three years, starting as an infrastructure engineer. I’m still an infrastructure engineer, and I work with customers every day to build different solutions based on their needs.
Initially, I primarily worked with network security and infrastructure customers. But as we saw the AI wave coming, we started focusing more on customers who wanted to adopt Gen AI products and solutions.
I didn’t move into an AI role, but I work with a lot of AI services and the AI engineers who work on the features of those services. My job is to work with customers and product teams to deliver technical solutions to customers. It’s a constant feedback loop that determines whether the solutions we’re building are right for the customers we serve.
Our job is to know how these products work. As you work on a product, you may identify feature gaps or bugs, and you will need to collaborate with the product or engineering team.
I’ve always had the same role, but the nature of my work is changing because of everything that’s happening in the AI space. AI products are in high demand and require a lot of training to deliver.
I spend 1-2 hours training each week
The more AI advances, the harder it will be to catch up. As the pace of innovation in AI increases, the role of engineers has shifted from being skilled to continuously adapting at scale.
Just being conscious of everything that’s going on in the technology industry and what we have to do with our customers has changed dramatically from a year ago. Back then, you had to perform within known constraints. But as time passes and AI rapidly evolves, those boundaries will dissolve and we will need to spend more time learning about changes in this area. We must now overcome an ever-expanding problem area together with our customers.
I spend about 1-2 hours a week upskilling on new concepts. We have a lot of in-house training that you can take advantage of. So, see if there’s anything new you’d like to learn and if it would be useful for your job.
Developing a deeper level of understanding of high performance computing, AI observability, model performance benchmarking, and the underlying architectures of GPUs and TPUs.
can be overwhelming
Google’s culture is about constant learning. We learn about new tools and model versions every day. That motivates me to keep learning. In order to do our best in front of our customers, we also need to improve our skills.
But with today’s advancements in technology, we feel we need to know everything. If you don’t learn, you might be left behind.
In reality, it is virtually impossible to know everything about changes that occur at an exponential rate. To maintain effectiveness without burning out, I prioritize intentional depth over exhaustive consumption. By focusing on what you’re truly interested in, you can make your learning an investment in your expertise rather than just a chore to “catch up.”
If you read too much, you’ll feel overwhelmed and won’t be able to retain all the information you’ve taken in. We are bombarded with information and have to figure out where to spend our time and what is most beneficial to us.
Are you an engineer and have you noticed a change in your work? We’d love to hear from you. Send an email to the reporter from a non-work device via email (aaltchek@insider.com) or via the secure messaging platform Signal (aalt.19).
