What does it mean to work in GenAI at Elastic?

Serena Chou, Product Manager at Elastic®, wasn’t always interested in artificial intelligence. In fact, it wasn’t even on her radar when she began her career in technology. But today, every technologist knows AI is rapidly moving to be an expected component of software applications. The challenge to overcome is how to use this technology quickly and effectively, she says. 

“To invent new things and improve the world at large, this involves AI,” Serena says. “I’m excited about it—I think it’s amazing. It’s one of many innovations to come.”

She joined Elastic two years ago and for the first year worked to ensure the company had the right foundation in its Search solution to integrate AI in a meaningful way. 

“Elastic has been looking at AI for quite some time and recognizes that customers need a framework to responsibly use it,” she says. “We’ve had machine learning teams for more than two years training models, enabling model management, and incorporating vector search into the platform. With that expertise, we are better positioned than ever to meet customer needs.”

Now, we are taking these investments and pulling them into Elastic’s Search solution experience, she says. “We have the market’s trust and we’re delivering on that.” 

As part of the Search solutions team, Serena coordinates with the machine learning team and search platform team to execute on a roadmap to pull capabilities into a cohesive package that developers and customers can use to apply Gen AI technology on their private data. 

“Customers can use these tools in an intuitive and easy to use way,” she says. “They can quickly onboard and understand the value even if they aren’t data scientists.”

The use of GenAI in search changes the way people can search data, accelerating their transformation of retrieved data into knowledge, Serena says. 

“They need information at their fingertips packaged in a format they can understand,” she says. “At the heart of that is the need for a fantastic search experience on their data. AI isn’t the end goal, it’s a tool to fuel a search engine that can meet that need.”

And AI isn’t at a point where it can serve every need, Serena says. “It needs to be leveraged alongside information retrieval standards that have been used for years to truly solve problems.

Companies are in varying stages of their own AI journeys — Elastic is working on enabling companies to use it no matter where they are in that journey. 

“Some are really excited about the opportunity but aren’t in a position to reap the full benefit,” Serena says. “They need composability. Being able to leverage different components in the Elastic platform interchangeably is a great advantage because it helps them walk through the process and evolve without a full commitment to re-architect what they already have.”

“For others, they want all of the benefits immediately without any additional work on their end. We have product capabilities to help these folks, too,” she says. 

With the pace of innovation these days, Serena thinks AI is going to be wildly different in the next few years. 

“There’s an evolution here that we are in the midst of discovering, and the pace of that evolution is staggering,” she says. “But the one thing we do know is Elastic has a critical part in that journey. For customers also exploring these technologies, they need a partner who will be committed to evolving at that same pace. Elastic is that partner. ”

Elastic has invested in making sure that there’s a secure, responsible way to use AI. It can take private data and provide context in a responsible way in a production ready environment, Serena says. 

“We have a deep understanding of the right way to use it [AI],” she says.

And to work in AI, you need to be an advocate for that responsible use.

“It’s important to not get lost in the buzzwords,” Serena says. “You need to have a pragmatic viewpoint to filter through and keep the customer in mind. 

Curiosity is also crucial when you’re a product manager in this field. 

“Stay open minded and challenge assumptions,” she says. 

From a more technical perspective, knowing various programming languages like Java and other common languages used with data science is beneficial. Serena recommends Python if interested in machine learning.

A knowledge of software system architectures can help you parse potential uses for AI and understand the challenge customers have with incorporating those technologies into what they’re building today, Serena says. 

Know where you want to spend your time in the chain of usage. Anyone looking to be a product manager needs to know their focus and their expertise to be effective, she says. There’s a wide range of problems to be solved, depending on your interests.

“For example, if you’re interested in providing a great product experience manifesting the potential of these types of tools to end users, they should look at how we’ve implemented that in our Security and Observability solutions,” Serena says. “If you’re interested in looking at platform capabilities and how devs use a Python ecosystem, look at the work coming from the Search solution team. Interested in researching search algorithms and fine tuning Lucene, or helping Elastic think of the next generation of models that we will offer? That’s all coming from our platform team.”

As for the future of AI at Elastic? 

“In the future, I think you’ll see us being a partner, providing guardrails, and investing in machine learning to bring new innovations where people can search at speed and scale with the right accuracy.”

Want to have an impact on a company leveraging AI? Browse open roles. 

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