- Supreet Kaur received a job offer from Microsoft after interviewing at 15 companies.
- After two years of working in AI at Morgan Stanley, Kaur saw a major shift in the market for AI roles.
- She emphasized the need for LLM experience, networking, and understanding a company’s AI needs.
29-year-old Supreet Kaur has interviewed at 15 companies over the last few months, and just received an offer from Microsoft.
Prior to securing her new role, she spent the last two years developing and managing data and AI solutions at Morgan Stanley. She said the job market for AI roles has changed drastically since she was on the hunt about two years ago.
While Big Tech CEOs are fighting over AI talent, some candidates are fighting for a spot in the increasingly competitive job market.
Kaur has a grad degree in data science, worked in AI at a big bank, and is an ambassador for Google’s WomenTechMakers program — and even she said she wasn’t hearing back from companies when she first started her job hunt.
Once Kaur made a few tweaks to her approach, she was able to start seeing results and eventually landed the Microsoft position as a cloud solutions architect. If you’re looking for a job in AI, Kaur said these are the four key things you need to know.
LLM experience is now an industry standard
Kaur said when she interviewed for AI positions two years ago, companies were looking for machine learning experience. Now, companies are looking to build AI products. She said companies are more eager to see that a candidate has worked with a chatbot or text classification system.
Kaur said generative AI or LLM experience is now a basic standard — and she didn’t start hearing back from interviews until she skilled up in this area.
Once Kaur saw how many recruiters were asking for this, she volunteered within an organization and completed a three-month LLM project. While many applicants looking to enter the field now participate in AI workshops or bootcamps, Kaur suggests doing a use-case project. Kaur created her own enterprise level project from the volunteer experience so that she could talk about it in depth in interviews.
Cold applications may not work this time around
Kaur said she didn’t send too many cold applications, but she didn’t hear back from the ones she did send. Instead, she said she spent her time networking and contacting recruiters. She said she aimed to send at least two messages and three to four personalized connection requests every day.
She also tried to spread the word that she was looking for a job by telling people in professional settings that she was on the market.
“The best way to look for a job is when you don’t need a job,” Kaur said. “You should go to events. You should go to meet-ups.”
Be specific
Kaur said companies have had a shift in mindset over the last couple of years. Today, they are looking for much more specific experience, Kaur said.
“When I was interviewing in 2022, people were more interested in what I had done in data science,” Kaur said.
“This time all my interviews were super specific on what the companies wanted,” she added.
With companies’ hiring portals overflowing with qualified applicants, Kaur said she needed to narrow things down. Kaur said she refined her search from product manager to solutions architect once she realized her first attempt was too broad.
Kaur also recommends networking with workers from the company you apply to and asking them what that company is looking for. She said this is crucial to understanding their needs and the kind of experience they specifically want in a candidate.
Having an online presence helps
Kaur also spent the last couple of years building an online presence.
She said she’s spoken at dozens of events and many of those led to interviews later on. It also helped her stand out in the application process.
“Some hiring manager during our interview said, ‘You’re the hundredth candidate I’m interviewing for this one position,'” Kaur said. “So it’s obviously very competitive so it’s important for you to stand out.”
Kaur said she started by contacting the university she attended and telling professors that she was available to speak about her experience. From there, she was able to start building her following and book events regularly including AI Summit New York, BNY Mellon, Re-Work New York, Women in Data Science Series, and Women in AI Series.
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