From the world’s largest tech companies to start-ups, everyone is looking for people well-versed with Artificial Intelligence (AI). But a career in this business is no cakewalk: A lot of mathematics, constant leaning and understanding human behaviour are just some of the ways to get a foothold in this fast-growing industry. We spoke to five AI professionals, who tell us that a career in this field is about many different things, from data analysis, text and image recognition to linguistics—and no, evil robots do not figure in the list.
Rohit Ghosh
AI researcher and founding member, Qure.ai
Ghosh, 26, spends his days looking at X-rays. “I am almost a semi-radiologist. I can easily read chest X-rays and brain series,” says this computer engineer from the Indian Institute of Technology, Bombay. Ghosh joined Nomura as a data scientist immediately after his bachelor’s degree, but quit after a year. “I was working on reducing risk and I surely impacted someone somewhere, but I couldn’t see the results for myself,” he says. He then spent six months in 2015-16 talking to tech start-up founders about the kinds of problems they were solving, and also did refresher courses on machine learning. “ I keep up by reading research papers on ArXiv.org and solving problems and challenges on websites like Kaggle,” says the Mumbai-based Ghosh, explaining why continuous learning is the only way to forge a career in AI.
In 2016, he joined health tech start-up Qure.ai as part of its founding team, working on deep-learning algorithms for radiology image processing. He is also a project evaluator and classroom mentor for AI courses on online platform Udacity, and a classroom machine-learning instructor for Mumbai- based ed tech company Greyatom. “My teaching assignments at Udacity and Greyatom help me formulate complex maths problems in a simple and step-by-step manner, as well as debug problems when students get stuck,” he says.
Biggest myth: “People think AI is some kind of magic tool that can solve all problems. It’s only a statistical tool. For an AI solution to be effective, it needs certain conditions fulfilled, like having plenty of data to work with,” he says.
What I love: “I get to work on maths and use it to work on something that saves lives in a very real way,” he says.
Money matters: Compensation can range from ₹10-₹12 lakh per annum for graduates with no experience, going up to ₹15-₹35 lakh per annum for professionals with a couple of years of experience
Sohan Maheshwar
Developer evangelist at Amazon, and city ambassador at City.ai
Maheshwar’s day job involves working with developers around Amazon’s Alexa platform. The 31-year-old speaks to developers around the country on building better voice applications. And, as the Bengaluru city ambassador for the global AI community City.ai, he organizes gatherings, inviting speakers to highlight trends, industry insights and practicable approaches in the field of applied AI. After completing his bachelor’s of engineering from Visvesvaraya Technological University, Belgaum, in 2009, Maheshwar worked as software developer at TCS and Taram Software before joining InMobi in 2013 as a developer marketer. In 2016, he moved to Bengaluru-based chat messaging company Gupshup. Last year, he joined Amazon. If you want a career in AI, “keep your eyes and ears open, follow the latest research, attend conferences. Many cities have AI communities, both online and offline, and lots of free resources. I got the opportunity to be the city ambassador of City.ai after I met the founder of City.ai at a conference in Vienna, and heard from him that he was looking to start in Bengaluru,” he says.
Biggest myth: “That AI is about talking to robots. It’s about speech data, linguistics, and these are just a subset of AI technologies,” he says.
What I love: “Interacting with developers every day, and teaching people to build stuff is exciting,” he says.
Money matters: Compensation can range from ₹10-₹60 lakh per annum.
What Haptik looks for in AI professionals
Aakrit Vaish, 32, CEO and founder of AI-based chatbot platform Haptik, takes hiring interviews very seriously. “You need to be very, very good at maths to be good with AI, because this field essentially involves looking at a problem, understanding aggregated data, and making sense of it. It’s about predictions and solving problems,” he says. Vaish says 50% of any interview conducted by his start-up is based on a case study. “We judge a candidate’s problem-solving skills. We also like to look at specific experience in data science, like modelling work or algorithm design, and then ask specific questions on them,” says Vaish. A practical familiarity with AI helps—try using chatbots or Alexa at home.
Paul Meinshausen
Data scientist in residence, Montane Ventures
Meinshausen, 33, is an anthropologist by education with a bachelor’s degree from the University of Louisville and a masters from Middle East Technical University in Turkey. “I’ve always been interested in understanding why humans and groups of humans act the way they do,” he says. From understanding people to helping machines understand human processes, his choice of career was a logical step. Meinshausen started work in this field early in his career when he worked with the US army (2009-2011) on a series of projects to understand human behaviour in conflict areas like Iraq and Afghanistan. “This wasn’t somewhere I could go out to interview and observe people. So, I began working with large intelligence data sets instead,” says Meinshausen. Eventually, he went on to study data science as a researcher at Harvard and become a data scientist fellow at Chicago University, studying statistics and programming. After stints at analytics firm Teradata in Singapore, and Housing.com and Paysense in Mumbai, Meinshausen now works at a Bengaluru-based early-stage venture fund Montane Ventures. His job involves identifying innovative AI start-ups which his firm can work with.
Biggest myth: “That AI is man versus machine. At a chess or Go game where a human plays against a computer, you see one person with a cup of coffee playing with a machine. But behind the machine is a team of at least a 100 scientists and so many pieces of systems, all built by people who capture data, clean it, build feedback loops and implement it,” he says.
What I love: “I get to work with interesting start-ups and model the world by looking at its different pieces for the problem I am working on,” he says.
Money matters: Starting salaries range from ₹10-20 lakh per annum and can go up to ₹25 -40 lakh per annum for 5-10 years of experience.
Six lessons from a post-millennial on getting a job in AI
As a graduate it is not easy to get a job in AI, unless you can show the right experience,” says Shavak Agrawal, 21, who works as a data scientist with Microsoft . A bachelor of engineering in computer science from BITS Pilani, Agrawal made sure he opted for machine learning courses during his degree. He had a summer training stint as a research intern at IBM Labs, Bengaluru, and a second one with Quant One Technologies, a Kolkata-based firm that develops trading algorithms. For his final- year project, he worked for six months with Flipkart in Bengaluru, building AI frameworks to identify and predict anomalies in the
e-commerce company’s supply chain. Here are six things he learnt:
■ Introspect early about why you want to get into machine learning. This is something interviewers ask and you should have an answer for it. For me, this was triggered by the famous Target store case study, where an algorithm on shopping patterns uncovered that a young woman was pregnant even before her family knew.
■ Keep yourself constantly updated on the latest research. I follow ArXiv.org, which publishes the latest research papers, and computer scientists like Yoshua Bengio and Yann leCun, besides blogs like Distill.pub.
■ Do online courses.The machine learning courses on Coursera by Andrew Ng, co-founder of Coursera and adjunct professor at Stanford University, is a great start.
■ Spend time on understanding algorithms and how they really work , instead of accepting the algorithm as a black box.
■ Be selective about the kind of job you choose. Today there are AI jobs advertised everywhere, but you need to look carefully and in detail at the kind of work or product that is being worked on and at the background of the people working on it. It’s important to have good mentorship—someone who can teach you on the job.
■ Have a good understanding of linear algebra, probability, statistics and other core maths concepts. Machine learning involves complex functions with different variables. You tweak different parameters and run experiments with them. You should be comfortable doing that.
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