How to Build a Career in Artificial Intelligence

Increasingly, artificial intelligence is playing key roles in driving business value, boosting efficiency while reducing costs. This trend increases the demand for technology talent capable of building and maintaining AI models, which requires a mix of evolving software coding capabilities and soft skills. We spoke to five leading artificial intelligence experts to determine how to best enter a career in space.

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The AI ​​labor market

With so many areas of artificial intelligence being developed today, it makes sense that space-bound job creation will continue to evolve along with space.

Szymon Idziniak, machine learning engineer at STX Next, suggests dividing the AI ​​job market into a few key roles:

Machine Learning Engineer – Someone who has a strong IT/engineering background and has the ability to write entire software themselves. “They also need to have a strong machine learning background and understand how something works before they start using it.” ML/deep learning researcher – Someone who has a strong math and machine learning background. “They must have the ability to develop new neural networks from scratch to solve difficult programs. They will often lack software engineering knowledge, but may be supervised by another developer,” Idziniak said. Additionally, this role is primarily focused on just creating and evaluating new models. Data Scientist – Someone with a background in statistics, IT and mathematics. “They often lack a software engineering background, but are good at analyzing data and creating reports. They will have a good ability to write in notebook and provide useful insights from data, use more classic ml approaches like XGBoost, scikit-learn, Pandas and NumPy. MLOps Engineer – Someone who has a strong DevOps background with knowledge of how to write code and also understands basic machine learning approaches. “They will mostly focus on how to use cloud providers in real projects.”

The best new roles in AI

When it comes to determining the job roles set to drive the most value from AI-powered capabilities, experts see much of this value-add coming from product development related to customer service and compliance.

“Rapid engineering is a role that has recently emerged with the development of generative AI technology. Agile engineers are responsible for designing and refining the incentives or inputs used to generate text or other outputs from AI models,” says Kunal Purohit, chief digital services officer at Tech Mahindra.

“Another role I can see emerging is AI product managers, who will be responsible for the development and management of generative AI products, from idea to launch. The AI ​​conversational designer, meanwhile, can play another role who is an expert in designing conversational interfaces for AI-powered chatbots, virtual assistants, and voice-activated systems, ensuring smooth and engaging user interactions.

“As the unprecedented power of AI and generative AI comes with many security and liability issues for its use, an ethical engineer will play an important role in ensuring transparency, fairness and avoiding any unintended biases in the generated output. Then the AI ​​trainer will also enter the mainstream career, as they will have responsibility for training generative AI systems, teaching them to recognize patterns and generate output that matches specific goals.”

As each industry explores ways to harness generative AI to improve their business operations, many industry-specific roles may emerge and evolve over time, and as generative AI continues to unfold its potential.

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Programming languages

According to Martin Butler, professor of management practice at Vlerick Business School, Python will be king when it comes to starting and maintaining a career in artificial intelligence because it is widely used and easy to learn.

He explains: “It provides an excellent entry into programming for beginners, but it is still used in complex scenarios by many experienced programmers. Experienced developers will learn to add additional languages, often context specific, but Python is the best entry point that can take you very far on the AI ​​developer journey.

When considering which programming languages ​​aren’t worth learning, Butler cites typical scripting languages ​​like JavaScript, PHP, Perl and Ruby: “These are all very capable scripting languages ​​that are powerful in their own right, but not necessarily for AI applications not.

“The exception? Python, as it can be used for both scripting (ie easy to learn) and programming (more suitable for AI work).

Top technical qualifications

When applying for AI-focused job roles, it’s important not to underestimate the challenges that the exciting but demanding technologies can bring—especially for those new to the space.

“To put this into perspective, the UK’s main AI research organisation, DeepMind, used to recruit the top computer science graduate from the University of Cambridge every year, but they stopped that practice several years ago,” says Clare Walsh, director of education at the Institute of Analytics.

“They found that even the most promising, hard-working 21-year-old was unable to cope with the demanding environment of experimental AI. As a company policy, DeepMind now requires a PhD qualification as a minimum to get a job there.”

To become a chief data officer—a position that increasingly involves working in AI—a degree in a relevant field is likely to be required as a minimum. Walsh continued: ‘There are different routes to do this. For example, to apply for Chartered data scientist status, a gold standard in the field, you will need a minimum degree.

“There are of course many people in service who have gone through different routes. Realistically speaking, this current ‘AI summer’ or period of rapid expansion of AI only began in earnest around 2016. For the first few years you had to be a PhD researcher, or perhaps complete an MSc to have access to university training in these fields.

“However, the field is more formally regulated and although there will be older, more experienced coders who have no formal training, it would be risky to assume that the best jobs will still be available to anyone in, say, 10 years’ time . “

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Retraining as an AI specialist

One last question remains: is it ever too late to retrain as an artificial intelligence specialist?

“I don’t think it’s ever too late,” said Heather Dawe, UK head of data at digital consultancy UST. “Diversity in the people who develop and use AI is so important – people who have done other things in their career before or who have lived and worked in other ways bring diverse experiences and knowledge together and that is a strength in itself.

“Furthermore, as the uses of AI are likely to become more pervasive, we will all increasingly come into contact with it. I think society will in turn become more aware of this and this familiarity will hopefully encourage more people to consider retraining as their interest is piqued.”

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