AI is the fastest growing field in enterprise tech. Here's how to get an AI job you will love.

For senior IT people, 2019 may not look to be the happiest of new years. Many experienced technologists are finding their roles outsourced, with other employers looking for only younger (read: cheaper) employees. "I had three jobs in three years," Mike, a 50-something New York-based IT specialist, told me a year ago. "They’ve all ended with even new hires being let go and the work outsourced. I had to go before a judge to explain my financial situation, and he said I should take a class to update my skills. As if that would fix it."

As it turns out, there is a class. AI job listings have become the fastest growing category on LinkedIn, and Indeed is packed with listings. But most job requisitions seek a computer scientist type with a PhD in neural networks or some other years-long study. The trick is to look past those, and you’ll find that what many companies need can’t be outsourced or given to a freshly minted college grad: an IT pro with enterprise-scale experience who also knows how to deliver on a machine learning project.

Machine learning is where the jobs are

Here’s the secret: There are plenty of AI-related jobs that aren’t advanced science but simply applying new machine learning features from cloud services giants to familiar IT environments. "Most ML jobs aren’t about advancing ML technology and algorithms," says Ross Mead, founder and CEO of robotics software startup Semio and an industry consultant in AI with a PhD from the University of Southern California. "The money in AI for most companies is using ML for better business intelligence."

That means using turnkey ML packages to analyze internal data—customer behavior, sales, etc.—to look for patterns that indicate likely business success. Machine learning is different from deep learning, the more esoteric field of AI that it is often confused with. As Mead puts it, deep learning is like teaching a computer the rules of chess so it can figure out new strategies. Machine learning is like feeding it the records of 10,000 games so it can calculate the confidence level that a particular move leads to a winning outcome. "It’s really a big data project," he says. "They need to get enough qualified data together to train the ML engine."

We don’t get clients asking for AI people, or machine learning. People with AWS certificates get snapped up, though. Chris Bloomquistpartner, Viri Technology

Amazon’s world-domination plan includes machine learning

"We don’t get clients asking for AI people or machine learning," says Chris Bloomquist, a partner at Seattle-area recruiting firm Viri Technology. "People with AWS certificates get snapped up, though." Among cloud-based services, AWS is the go-to platform for many startups, but it’s also used in enterprise-level IT infrastructures.

In late November, Amazon put its internal training courses for the machine learning features in AWS online for free, to make AWS engineers easier to find for ML projects. The company’s general manager of AI, Matt Wood, explains, "There are more than 30 self-service, self-paced digital courses with more than 45 hours of courses, videos, and labs for four key groups: developers, data scientists, data platform engineers, and business professionals."

Bloomquist says, "With the courses now free, the question for candidates is more, 'Why haven’t you taken them?'"

In fact, Bloomquist and Mead agree that most corporate projects are more in need of a seasoned IT pro than a new college grad equipped with a degree in AI science. Four IT fields ML relies on are the kind one doesn’t learn in college but in a few years on the job:

Cloud computing: Nearly every ML project is a cloud project.

Nearly every ML project is a cloud project. Scalability: There’s no such thing as a "small" machine learning project.

There’s no such thing as a "small" machine learning project. App security, cloud security: Machine learning systems are based on and driven by personal data, enough of it that even anonymized data can sometimes be triangulated to a specific individual.

Machine learning systems are based on and driven by personal data, enough of it that even anonymized data can sometimes be triangulated to a specific individual. Edge computing/Internet of Things: Much of the data analyzed by business intelligence systems is or soon will be uploaded by IoT devices that report their location and status for better tracking and monitoring.

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Platform-specific training—for free

To grow the number of technical people familiar with their offerings, all five of the major cloud vendors—Amazon, Apple, Google, IBM, and Microsoft—offer online courses. They’re aimed not at beginners but at experienced programmers and code-adjacent workers, so IT pros can add hands-on ML training to their skill sets:

You can find AI, or AI may find you

Mike, who couldn’t find work in New York, ended up moving to Dallas for a job building a BI system that uses Azure, which leverages his years of Microsoft platform experience. But if you’ve already got a job, there might be a better path than looking for an ML project elsewhere: Stay put. AI is inevitably working its way into enterprise infrastructure and operations. AI operations (AIOps) platforms, as Gartner calls them, "utilize big data, modern machine learning and other advanced analytics technologies to directly and indirectly enhance IT operations (monitoring, automation, and service desk) functions with proactive, personal, and dynamic insight."

It's not just DevOps that’s being made over. IDC forecasts that corporate spending on AI systems in 2022 will be more than triple that of the past year. The hottest areas of application: customer service automation, threat intelligence and prevention, sales process recommendation and automation, and automated preventive maintenance. AI, mostly machine learning, has the potential in all these areas to increase the number and size of sales and prevent revenue losses due to downtime and disruption.

That’s why if your real goal is a sustainable career, as you build your AI chops, you also need to change your focus to how these new technologies are best for business, advises Gnu Talent executive recruiter John Hughes. "Yes, companies with a focus on AI or ML will need that specific experience in their CIO or CTO," he says. "However, most companies need a technology leader who's more broad than that. What they are asking for now is a tech leader who's really a business leader first and foremost, so they can help the business place the right technology bets that line up with their business strategy."

If you’re looking to boost your immediate hiring appeal, Amazon’s ML courses for AWS seem the best bet. But to become a long-term leader who can’t be outsourced, the way to future-proof your career in an AI world may be not to jump to a new workplace, but to take the lead in bringing AI into yours to show where it’s good for business.

How to get a job in AI: Lessons for leaders

AI for most companies means machine learning. Take Amazon’s free ML courses or those for another cloud service if your employer uses it.

Focus on how machine learning can deliver better business intelligence. Most "AI" jobs don’t involve developing machine intelligence, but rather applying turnkey ML services to improve efficiency, accuracy, or profitability by analyzing patterns in relevant company data.

Make your goal not just moving on, but moving up. Combine your prior experience with enterprise-level IT with newly acquired ML chops to step up from technician to business leader—a veteran whose tech-savvy counsel is essential.

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