A Career Steeped in Tech: from Hardware to Generative AI

Bringing systems—and innovation—to life has been a central theme throughout Chu Lahlou’s career. She started on the hardware side studying electrical engineering, then continued learning in areas from semiconductors to data science. Most recently, she has been exploring generative AI as an executive advisor at Booz Allen.

Chu talks about her journey from engineering to AI, the flexibility and support that made it possible, and how others can similarly find their own path in emerging technology fields. 

During her studies at the University of Virginia and the Massachusetts Institute of Technology (MIT), Chu gained hands-on experience through internships and research projects: in controls engineering with Dominion Power, in circuit research with NVIDIA, and in developing a framework for low-power circuits for signal processing, video coding, and computer vision as an MIT research assistant. She then spent a few years at Texas Instruments in areas such as analog IP development and automation.

“I worked at a hardware level to learn how to support amazing new algorithms for things like computer vision and object detection,” she says. “I got more interested in the application itself, understanding the algorithms and learning the correlation between data from a spatial and temporal perspective.”

In 2018, she moved from a semiconductor manufacturing company to Booz Allen. As a lead data scientist and solution architect, she drove the development of our hardware-accelerated AI and machine learning solutions across markets. She also helped our federal clients adopt, build, and deploy deep-learning applications in computer vision, natural language processing, cloud and edge computing, and scientific modeling.

“This field can be very versatile. Data science, machine learning— it all can be applied and used in many different industries,” Chu says.

In her data scientist and solution architect role, she appreciated “being able to do things from end to end: seeing the problem, addressing the challenge, coming up with the solution, and delivering it to see the results.”

Chu then took on a new position outside of Booz Allen, as team lead of the Microsoft “AI Rangers” in the Americas. This group of specialized AI cloud architects developed prototypes in areas from medical imaging to supply chain optimization, and Chu managed a variety of generative AI, data science, and machine learning initiatives.

“It was a great opportunity for my technical growth,” she says.

In 2023, Chu returned to Booz Allen and put her multifaceted knowledge to work, bringing talent and cutting-edge technology together to grow our generative AI offerings.

Part of Chu’s role is to ask a lot of questions and have conversations with colleagues across the firm. What do our clients want and need? What are common use cases for capabilities like intelligent knowledge extraction? What are the pros and cons of different architectures? How can technology adoption be accelerated and, just as importantly, governed?

“How do we use AI responsibly and safely? As emphasized by recent White House executive orders, this is a topic that cannot be stressed or emphasized enough,” Chu says. “We need guardrails, checks, processes, and tools to understand and thoroughly examine the results that our AI solutions, and not the models themselves, deliver.”

Chu sees Booz Allen as a place to grow in many ways. “The flexibility was great as I became a mom and grew my family,” she says. “And it’s also a culture where people are encouraged to step out of their comfort zones, speak their minds, and contribute.” 

Diverse perspectives is particularly critical in the area of responsible AI to mitigate bias in solutions. “Not everybody is the same. Every leader brings their own unique perspective,” she says.

And it’s just as important for these leaders to be visible for the next generation. “Maybe a leader is worried they don’t have a degree in AI or they’ve never worked in AI before,” says Chu. “But sharing and being open about your own journey and career can be helpful for those looking to get into your field.”

Her advice to those interested in AI: Just get started.

“It’s not like AI is a secretive language or something you have to go to school for,” she says. “There’s a lot of content about AI out there for you to explore with open source and how active the research and tech community are today.”

If you’re changing careers, “start with something you’re familiar with and find a common theme,” she suggests. “In my case, I was working on low-power Internet of Things for hardware, then offered this expertise for adding AI components on top of it, like edge deep learning inference and federated learning. Getting something on your portfolio and finding your specialty will give you confidence, especially if you’re new to the field.”

At any stage in your AI career journey, Chu advises, “Do your homework, get on a side project with a group of people you like, and be willing to get your hands dirty.”