Artificial intelligence (AI) involvement can now be found within almost every area of business operations, from employee-facing tasks to customer service. This growing trend of end-to-end automation — boosted by generative AI capturing the imagination of organisations in recent times — will make way for the creation of new technology job roles. Here, we take a look at the new technology jobs that will be needed for AI, and the key skills required for those roles.
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Emerging job roles
The following technology jobs and duties have been cited by tech recruitment experts as future business innovation trends set to emerge in the next few years.
Contact centre staff
As artificial intelligence is increasingly implemented into business contact centres, in the form of voice assistants and chatbots among other tools, human employees will need to learn how best to use the technology, and adjust processes accordingly. It is widely predicted that the most successful organisations will offer human-led customer service augmented by AI, for maximum personalisation.
AI-powered contact centre capabilities can allow business employees to retrieve and use interaction insights more efficiently, allowing for improved tracking of customer needs.
ML researchers and developers
“These are data scientists who look at the data, try and generate insight, and then also create the algorithms which lead to an AI model,” said Kumar.
“Then, there are the ML developers — a slight distinction from researchers. These will know how to build those algorithms, use them and maintain them.”
Kumar went on to state that together, ML research and development positions are gaining traction, from start-ups up to key industry players. Going forward, these roles look set to advance an evolution of data science as we know it.
“However,” he added, “you still need developers for integration of algorithms into your legacy stack, for this strategy to be effective. This will lead enhancement of the current developer role.”
NLP scientists and engineers
Amidst the rise in interest in generative AI innovation since the public launch of ChatGPT from OpenAI, natural language processing (NLP) is set to see job growth in the near future.
According to Salvo Depetro, director of technology and change at Barclay Simpson, his experience in recruiting for AI experts since 2017 has shown a natural evolution towards job roles involving NLP, as well as computer vision.
“I was probably one of the first recruiters specialising within R&D in London, and I’ve seen a rising demand in the last year or so for NLP scientists and engineers focused on text to speech or speech to text,” said Depetro.
“There has always been demand for these roles in the past few years, but since ChatGPT entered the market, businesses have been focused on finding this high level of tech scientist.”
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Also emerging as a result of the generative AI boom are prompt engineers, specialising in creating instructions for chatbots that can drive efficiency for day-to-day tasks across the organisation. With the technology being susceptible to flaws including bias and misinformation, prompt engineers need to look out for, and mitigate such technical difficulties through algorithm training. After all, generative AI tools are only as accurate as the data fed into them.
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Now and in the future, AI job staff will need to have extensive knowledge of an array of coding languages. While Python is likely to be the most widely used regarding model testing and maintenance task, other programming tools are bound to come into play, too.
“When looking at the role of AI-facing data scientists, they will know how to analyse data and look out for any linear regression. But they also still need to be well versed in the programming side,” said Kumar.
“The ideal technical skill set for developers, as of now, includes knowledge of cloud technologies; statistical models; some Python for examining the algorithms; and tools like R, for data analysis and SQL.”
Further programming languages that would prove useful in the space include Lisp – specialising in support for symbolic computation that aids AI research and development – and Julia, for data analysis and visualisation tools, as well as numerical solutions.
Machine Learning Operations (MLOps) is core to the engineering of machine learning algorithms. An area of technical processes emerging in software companies, it includes design, development and continuous testing of models.
“MLOps involves maintenance of the infrastructure for machine learning or AI. I believe there is a mix of capabilities needed there,” Kumar added.
“Not only is knowledge of programming languages necessary, but also CloudOps and DevSecOps skill sets.”
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With the possible societal impact that can come into play when dealing with customer-facing AI solutions — including those powered by generative AI — soft skills such as creativity, critical thinking and empathy will be vital for ensuring accurate and valuable models are maintained. No technical skill is going to be capable of replacing the need for skills relating to interpersonal and mental aspects of the job.
“While deep technical skills have been the backbone of advancements to date, they won’t be the only skills that are important for advancements in the future,” said Claire Hamilton, UK head of talent acquisition at Capgemini.
“Organisations must consider softer skills, personality strengths and experience – all of which could bring drive and focus to an existing development team.”
Building the best possible team
Hiring and retaining talent in artificial intelligence will ultimately call for a mixture of high-level technical skills and soft skills, as well as staff from different demographic and sociographic backgrounds to ensure the diverse and inclusive approach needed for long-term innovation.
“With AI being a major focus for the next decade, we’ll begin to see organisations grow and diversify their teams to rely on a variety of skills to further the evolution of AI,” said Hamilton.
“Deep technical roles, such as software developers and engineers will still have a significant impact on the industry, however today’s skill sets alone, will not be enough to scale this technology. Disruptors and challengers will also play a major role – one simply won’t be able to exist without the other.”
Depetro advises: “Identify what you want and what you need first. From what we’ve seen in larger organisations, for example financial services institutions, is that they’re initially looking for machine learning scientists. But then talent hired end up working in a role more generally focused on software engineering job.
“By misleading candidates, ultimately you will upset people because this is not the purpose of the day-to-day job. You really need to clearly and specifically determine what you want, and avoid creating a job title just because you want to make that job sound fancier.”
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