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Time of India
5 days ago
- Business
- Time of India
Indian AI engineer working for top tech company shares advice for techies who want to make a career in AI
From Bikaner to Big Tech Strategic Moves: Internships, Higher Education, and Networking Skills Over Degrees: Her Advice to AI Aspirants India's Growing Edge in AI Talent As artificial intelligence reshapes industries worldwide, thousands of tech professionals in India aspire to carve out a place in this fast-growing field. With global tech giants aggressively hiring top AI talent, including offering multimillion-dollar packages, the pathway into AI roles can seem both promising and Goyal, a 28-year-old AI and machine learning engineer based in Seattle offered clarity and first-hand insight into this space to Business Insider. Her journey from a small town in Rajasthan to working at one of the world's leading tech companies serves as a valuable case study for those looking to break into from Bikaner, Kriti had initially aimed for a career in medicine. But everything changed when she saw a video featuring global tech leaders such as Bill Gates and Mark Zuckerberg. The idea that coding could turn ideas into real-world products inspired her to switch to technology. Today, she is part of the Foundation Model main framework team at a top U.S.-based tech firm, where she writes core infrastructure code that helps machine learning models identify patterns in unseen began with an internship at her current company's India office but soon realized that major business decisions and innovation were happening at its U.S. headquarters. Rather than opt for internal relocation, she pursued a master's degree at the University of Wisconsin–Madison. This decision not only enriched her technical knowledge but also allowed her to build connections that would later help her secure a U.S.-based graduation, Kriti bypassed job boards and contacted former managers directly. Her ability to pitch her AI solutions and get them adopted during her second internship made a strong impression, ultimately securing her a full-time emphasizes that while a master's degree helped her professionally and with immigration, it's not the only path to success in AI. The industry, she notes, is evolving to value skills and outcomes over formal qualifications. Demonstrating capability—through projects, internships, or networking—is now more important than daily routine includes a mix of research, collaboration, and coding, with hands-on work being her favorite. She encourages young tech professionals to focus on learning by doing, building strong networks, and staying adaptable in a rapidly changing global firms like Meta push compensation limits for elite AI talent, India remains a favored destination for AI hiring—particularly in implementation, engineering, and mid-tier research roles. According to AMS, Indian AI professionals often cost only 15–25% of what companies pay in countries like the U.S., making India attractive for scaling such as Roop Kaistha and Sunil Chemankotil highlight India's dual advantage: cost-effectiveness and a growing talent pool. While high-end research roles still skew toward the U.S., India is steadily moving up the value chain. However, as Neeti Sharma of Teamlease Digital points out, significant investment in upskilling and innovation is needed for India to compete at the highest levels of AI R& ultra-premium AI roles with eight-figure packages may currently be concentrated in the West, India's position in the global AI ecosystem is strengthening. Initiatives like the IndiaAI Mission and a maturing startup landscape are expected to further fuel this rise.
Yahoo
6 days ago
- Business
- Yahoo
I'm a 28-year-old AI engineer in Big Tech. Here's my advice for others who want to break into this growing field.
Kriti Goyal leveraged her master's degree to advance her AI career in the US. She pitched projects internally during her internship that were adopted to help secure a full-time offer. Higher education aids in tech careers, but networking and skills can also open doors. This as-told-to essay is based on a conversation with Kriti Goyal, a 28-year-old AI machine learning engineer based in Seattle, about her journey into her current role and her daily schedule. It's been edited for length and clarity. I was mostly raised in the small town of Bikaner, Rajasthan, in India, and I always thought I would study medicine until my cousin showed me a video that changed my life. It was a video with Mark Zuckerberg, Bill Gates, and other tech rockstars, about how coding is the quickest way to convert an idea into a product. That video was a very big turning point in my life and career. I'm now part of the Foundation Model main framework team for a major Big Tech company in the US. I recently completed five years with them, during which time I've held four different roles. I used my master's to move to the US and further my career. But whether or not a higher degree is necessary today is complicated. There are many roles on machine learning teams There are multiple rungs on the ladder of Machine Learning teams. The different roles include researchers, engineers calling on the machine learning models and building applications on top, and the core machine learning people who are developing the actual model itself. Finally, you have the infrastructure stack barrier, doing the product center toolkits to help machine learning teams. I work on building the foundation of machine learning models, which means I build code that trains software to recognize unseen data and create patterns. I started my tech career as an intern in India, but knew I had to come to the US to advance I originally interned at my current company in India. I enjoyed working in India; the work was great, but the core business decisions and figuring out the strategy of the next project were happening at the company headquarters here in the US. I had no intention of moving to the US earlier. I was quite happy in my country. But overall, I kept feeling like I wasn't doing the best I could in my career because of living so far from the core business decisions, and I decided I wanted to make the move. I used my internship and master's to get further in AI engineering I had two ways to go about moving to the US: one was to try to move from within my company, or get a master's. There were two reasons I chose the master's path: the knowledge and extra specialty you can develop through projects and the connections you make. The biggest thing I took away from my master's program at the University of Wisconsin-Madison was definitely the people. When I got to the US, I knew a few people at my former company already from my time in India, so I reached out to a bunch of managers directly instead of applying on the job board. I got the interview for the Machine Learning engineering internship quite easily because they were aware of me and my work from before. Pitching my products internally helped me land a full-time AI engineering role When I started as an intern again, this time in the US, I did a few things that helped me land this job. I pitched my product to other teams internally to get it adopted. My manager kept telling me that, when they were fighting to get me into the company full time, that was a major thing they used. Now, as an engineer on the machine learning team, I like to segment my day into three parts. It sort of depends on the project life cycle, but usually, I start by researching. The second part is upstream and downstream check-ins with other team members and clients. I speak with people on other teams, saying, "Hey, this is what we can do, and does this work for you?" Everyone's favorite part is the third, which is basically hands-on building and coding. I'm lucky enough to spend most of my time being an individual contributor and focusing on coding. Higher education in tech still matters, but there are other ways I think it's possible now to skip that education stage. But I have seen a bias in hiring for specific teams, and it's not unbreakable yet. I was changing countries and cultures, and university was a great way to get through the immigration system and understand the culture. I needed it. If you want to be in academia and teaching, the higher education path makes sense. But if you want to build something fast, learning and networking can be done in many places. In a city like San Francisco or New York, you could hustle and get the networking benefits of a university and a structured system. You essentially just need the ability to prove that you can be good at the job. That doesn't really come from a degree. But I find there is usually some bias against applicants without a degree higher than a bachelor's. Do you have a story to share about AI or higher education bias in tech? Contact this reporter, Agnes Applegate, at aapplegate@ Read the original article on Business Insider Solve the daily Crossword
Business Insider
6 days ago
- Business
- Business Insider
I'm a 28-year-old AI engineer in Big Tech. Here's my advice for others who want to break into this growing field.
This as-told-to essay is based on a conversation with Kriti Goyal, a 28-year-old AI machine learning engineer based in Seattle, about her journey into her current role and her daily schedule. It's been edited for length and clarity. I was mostly raised in the small town of Bikaner, Rajasthan, in India, and I always thought I would study medicine until my cousin showed me a video that changed my life. It was a video with Mark Zuckerberg, Bill Gates, and other tech rockstars, about how coding is the quickest way to convert an idea into a product. That video was a very big turning point in my life and career. I'm now part of the Foundation Model main framework team for a major Big Tech company in the US. I recently completed five years with them, during which time I've held four different roles. I used my master's to move to the US and further my career. But whether or not a higher degree is necessary today is complicated. There are many roles on machine learning teams There are multiple rungs on the ladder of Machine Learning teams. The different roles include researchers, engineers calling on the machine learning models and building applications on top, and the core machine learning people who are developing the actual model itself. Finally, you have the infrastructure stack barrier, doing the product center toolkits to help machine learning teams. Please help BI improve our Business, Tech, and Innovation coverage by sharing a bit about your role — it will help us tailor content that matters most to people like you. What is your job title? (1 of 2) Entry level position Project manager Management Senior management Executive management Student Self-employed Retired Other Continue By providing this information, you agree that Business Insider may use this data to improve your site experience and for targeted advertising. By continuing you agree that you accept the Terms of Service and Privacy Policy . I work on building the foundation of machine learning models, which means I build code that trains software to recognize unseen data and create patterns. I started my tech career as an intern in India, but knew I had to come to the US to advance I originally interned at my current company in India. I enjoyed working in India; the work was great, but the core business decisions and figuring out the strategy of the next project were happening at the company headquarters here in the US. I had no intention of moving to the US earlier. I was quite happy in my country. But overall, I kept feeling like I wasn't doing the best I could in my career because of living so far from the core business decisions, and I decided I wanted to make the move. I used my internship and master's to get further in AI engineering I had two ways to go about moving to the US: one was to try to move from within my company, or get a master's. There were two reasons I chose the master's path: the knowledge and extra specialty you can develop through projects and the connections you make. The biggest thing I took away from my master's program at the University of Wisconsin-Madison was definitely the people. When I got to the US, I knew a few people at my former company already from my time in India, so I reached out to a bunch of managers directly instead of applying on the job board. I got the interview for the Machine Learning engineering internship quite easily because they were aware of me and my work from before. Pitching my products internally helped me land a full-time AI engineering role When I started as an intern again, this time in the US, I did a few things that helped me land this job. I pitched my product to other teams internally to get it adopted. My manager kept telling me that, when they were fighting to get me into the company full time, that was a major thing they used. Now, as an engineer on the machine learning team, I like to segment my day into three parts. It sort of depends on the project life cycle, but usually, I start by researching. The second part is upstream and downstream check-ins with other team members and clients. I speak with people on other teams, saying, "Hey, this is what we can do, and does this work for you?" Everyone's favorite part is the third, which is basically hands-on building and coding. I'm lucky enough to spend most of my time being an individual contributor and focusing on coding. Higher education in tech still matters, but there are other ways I think it's possible now to skip that education stage. But I have seen a bias in hiring for specific teams, and it's not unbreakable yet. I was changing countries and cultures, and university was a great way to get through the immigration system and understand the culture. I needed it. If you want to be in academia and teaching, the higher education path makes sense. But if you want to build something fast, learning and networking can be done in many places. In a city like San Francisco or New York, you could hustle and get the networking benefits of a university and a structured system. You essentially just need the ability to prove that you can be good at the job. That doesn't really come from a degree. But I find there is usually some bias against applicants without a degree higher than a bachelor's.
Axios
12-05-2025
- Business
- Axios
Tennessee among the first states to require computer science for high schoolers
Starting with the class of 2028, Tennessee high school students must take at least one computer science course before they can graduate. Why it matters: The new policy, which state lawmakers approved unanimously in 2022, was designed to prepare students for an influx of jobs that require a deeper understanding of technology and AI. The latest: Top business leaders are urging states nationwide to follow Tennessee's lead. More than 200 CEOs signed a letter this month urging state leaders to mandate artificial intelligence and computer science classes as a high school graduation requirement. Signees included leaders of American Express, Airbnb, Dropbox, LinkedIn, Salesforce, Microsoft, Yahoo, Zoom and Uber. State of play: Tennessee is one of 12 states that already have a computer science mandate in place, per How it works: The new graduation requirement kicked in for freshmen who entered high school last fall. In addition to the high school requirement, the law also required schools at every level to enhance their computer science offerings. By the numbers: The state logged a massive uptick in computer science enrollment even before the graduation requirement began. Middle and high school student enrollment in computer science courses sat at 32,893 statewide during the 2020-21 school year. It shot to 60,217 by the 2023-24 school year. What they're saying:"To be a full participant in the economy and the world, you have to be able to understand the technology that's driving the world," state Sen. Jeff Yarbro (D-Nashville) tells Axios. Yarbro helped push Tennessee to draft a plan for its approach to computer science education. The plan called for more course options statewide. "I want our students and people generally to be able to understand and shape these technologies more than be shaped by them." The bottom line: Students who attend high schools that offer a computer science course end up earning 8% higher salaries than those who don't, regardless of career path or whether they attend college, according to a report by the Brookings Institution. (The study examined the impact of giving students access to computer science classes, not of requiring it.)
Axios
05-05-2025
- Business
- Axios
CEOs push AI and computer science as a grad requirement
More than 200 CEOs on Monday signed a letter urging state leaders to mandate artificial intelligence and computer science classes as a high school graduation requirement. Why it matters: The letter follows President Trump 's creation of an AI education task force to expand students' exposure to AI instruction, as aptitude with the technology increasingly becomes a workforce expectation. "I think of it as a K-12 experience for students, where they're learning scaffolded knowledge about computer science throughout," said Cameron Wilson, president of which led the effort. Driving the news: Students who attend high schools that offer a computer science course end up earning 8% higher salaries than those who don't, regardless of career path or whether they attend college, according to a study by the Brookings Institution. (The study examined the impact of giving students access to computer science classes, not of requiring them.) The effects are more significant for students who haven't historically been well represented in computer science fields, like women, students from lower socioeconomic status backgrounds and Black students. Some new computer science classes have seen higher enrollment among these underrepresented groups, according to research released last month. From 2009 to 2016 a Java-programming course was the only AP Computer Science class offered. The launch of a newer, more broadly focused AP Computer Science class quadrupled the female, Black and Hispanic student test takers. What they're saying: "This is not just an educational issue," the letter said. "It's about closing skills and income gaps that have persisted for generations. It's also about keeping America competitive." Zoom in: CEOs who signed the letter included leaders of high-profile companies like American Express, Airbnb, Dropbox, LinkedIn, Salesforce, Microsoft, Yahoo, Zoom and Uber. Several coding education and ed-tech company CEOs also signed on. State of play: Graduation requirements are set at the state level. 12 states require students to earn credit in computer science to graduate from high school, per Those are Alabama, Arkansas, Indiana, Louisiana, Nebraska, Nevada, North Carolina, North Dakota, Rhode Island, South Carolina, Tennessee and West Virginia. Legislation in West Virginia was signed last week. By the numbers: In 2024, 35 states had plans for computer science education. That's up from 18 states in 2020 and two in 2017, per In 22 states, computer science classes satisfy math, science or foreign language credits for college admissions requirements. About 6.4% of high school students take computer science classes annually, according to the Computer Science Teachers Association. offers computer science and AI curricula free to teachers. Between the lines: Teachers have warned that AI could hurt students' critical thinking skills — but ongoing AI instruction would teach students how to safely and productively use these tools, Wilson said. "Using the AI technology itself and learning to use it — that's helpful and useful. But then there's actually understanding how it works, and then actually using the AI to build and create and solve problems," he said. "Those things fit on a spectrum of knowledge, and I think all of those things sort of fit together for what I consider to be true AI literacy." What we're watching: Trump on April 23 signed an executive order to advance AI education and foster youth interest "from an early age to maintain America's global dominance in this technological revolution for future generations." He established a White House task force on AI education to build public-private partnerships that expand K-12 AI instruction. The bottom line: "In the age of AI, we must prepare our children for the future — to be AI creators, not just consumers," the CEOs' letter said.
