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APAC career guide on AI

The multidisciplinary nature of artificial intelligence offers career opportunities not only in builder roles like engineering and data science, but also in AI ethics and applied AI

This article can also be found in the Premium Editorial Download: CW Asia-Pacific: CW APAC – Tech Career Guide: Artificial intelligence

At foodpanda, an online food and grocery delivery platform with operations across Asia, artificial intelligence (AI) is used by customer experience teams to predict whether a delivery is likely to go wrong. Its app developers also use AI to automatically recognise which app functionalities are liked or disliked by customers.

Over at DBS, Southeast Asia’s largest bank, teams of AI technologists oversee more than 300 AI and machine learning (ML) use cases – from sending 45 million hyper-personalised nudges to customers each month, to alerting small businesses of credit risks before problems emerge.

According to the recent AI digital skills survey commissioned by Salesforce, 72% of workers in Singapore are excited about the prospect of using generative AI for their job, while 98% say they want businesses to prioritise AI skills in their employee development strategy.

The increased interest in AI has driven a steady growth in AI careers in recent years, with AI and machine learning specialists topping the list of fast-growing jobs in 2023, according to the World Economic Forum.

“Working in AI is one of the most desirable career paths in the tech industry today,” says Ozzy Johnson, director of solutions engineering at Nvidia, noting that AI development is the foundation for accelerated computing, computer vision, speech analysis, natural language processing, and more.

“The industry around AI is exploding with career opportunities, yet a major obstacle for the industry is that there are too many jobs and too few qualified candidates. What this means is that anyone who is willing and able to develop AI skills, and take on the necessary training and education, is likely to become one of the most sought-after employees in the field of tech. This demand equates to high salaries, benefits and job security throughout the usual ups and downs of the tech industry.”

“Working in AI is one of the most desirable career paths in the tech industry today”
Ozzy Johnson, Nvidia

According to a global survey by Deloitte, 68% of executives reported a moderate to extreme skills gap, while over a quarter rated their skills gap as major or extreme. AI builders and engineers were the most sought-after, followed by “AI translators” such as business leaders who can translate business needs into AI system requirements, as well as user experience designers.

Indeed, the multidisciplinary nature of AI today also means there’s not just one career in AI, but increasingly more diversified roles depending on the context one is working in, according to Sebastian Rodriguez, chief technology officer at foodpanda.

“The field is fast evolving and different specialisations are already emerging,” says Rodriguez. “At foodpanda, we see our AI experts mostly coming from technical and mathematical fields. They often come from a computer science background, but many also join laterally from fields such as psychology, sociology, or other fields that have a quantitative tradition. What our new joiners have in common is that they are excited about data and what we can learn from it. They are true detectives at heart.”

Andrew Sklar, director of training and certification in Asia-Pacific and Japan at Amazon Web Services (AWS), notes that while there are multiple AI career options available today, the AI space is evolving almost daily, with roles such as prompt engineers being a new career opportunity that didn’t even exist 12 months ago.

“AI roles are also constantly evolving to keep pace with the development of the technology. What we know of AI careers today may look vastly different tomorrow as AI goes mainstream,” he says.

This is already happening with the hype around ChatGPT and other large language models, which has put ethical considerations surrounding their use to the fore, paving the way for emerging roles in AI ethics.

Petri Tuomola, head of data platform at DBS Bank, says in an ever-evolving AI landscape, AI ethicists will play a crucial role in ensuring that AI technologies are developed and deployed responsibly, considering ethical implications and fostering trust.

What an AI career looks like

AI builders and engineers are at the core of many AI initiatives as they build tools across the entire AI stack. For example, those in AI roles at AWS are charged with designing machine learning (ML) algorithms and systems, improving the performance of deep learning models, and working with customers to train and deploy ML models in the cloud and on edge devices at scale.

AI careers can also involve building and administering big data for ML models, assessing complicated datasets to recognise business trends, and developing and refining prompts for generative AI services.

“AI roles are also constantly evolving to keep pace with the development of the technology. What we know of AI careers today may look vastly different tomorrow as AI goes mainstream”
Andrew Sklar, AWS

“The need for integrity in data, robust security and a discipline built on engineering fundamentals will grow in importance as the use of AI accelerates. At AWS, our training offerings are uniquely equipped to support individuals seeking a career in this fascinating space,” says Sklar.

Some examples of AI careers, as pointed out by Sklar, include:

  • Software development engineers – who design, develop, test and deploy distributed machine learning systems and large-scale solutions for users.
  • Applied scientists – who work on systematic approaches to improve the performance of deep learning models.
  • Data scientists – who straddle the business and technical worlds with deep data analysis to achieve specific outcomes. In the field of ML, they design and build models from data, create and work on algorithms, and train models to predict and achieve business goals.
  • AI engineers – who develop the tools, systems and processes that enable AI models to be applied in the real world.
  • AI ethicists – who advise on ethical AI practices and guard against bias, unintended consequences, and ensure accountability within the organisation.
  • Prompt engineers – who develop text-based prompts to refine the responses of AI models.
  • Product managers – who design and launch new AI products and services to market and provide AI advisory and go-to-market advice, from use case and business case development through to AI ethics, including the responsible use of AI.

Skills and certifications

Gavin Barfield, vice-president and chief technology officer of solutions at Salesforce ASEAN, notes that with AI teams being increasingly cross-functional due to the growing number of real-world AI applications, there are opportunities for engineers to deliver business value from AI by solving real problems and driving results for customers.

To do so, they will not only need technical skills in areas like data science, machine learning, neural networks and prompt engineering, they should also develop industry skills and knowledge so they can collaborate more closely with the business and customers to identify use cases where AI can add real value.

To thrive as an AI engineer, DBS’s Tuomola says a solid educational foundation combined with a diverse skillset is crucial. Typically, AI engineers have a degree in computer science, software engineering, or a related field. In addition, they would need the following skills:

  • Proficiency in programming languages like Python, enabling them to implement AI algorithms effectively.
  • Expertise in machine learning frameworks such as TensorFlow and PyTorch, empowering them to build advanced AI models and systems.
  • Strong problem-solving abilities and analytical skills to join the dots in tackling complex challenges and devise innovative solutions, while taking a customer-centric lens.
  • In-depth knowledge of data structures and algorithms, which form the backbone of efficient AI implementations

While each of the above skills is valuable, Tuomola believes an understanding of AI and ML algorithms, and having the software engineering skills to apply them effectively, are paramount.

“Algorithms serve as the foundation of AI models and systems, and a profound understanding of them is essential for designing cutting-edge solutions that best fit our business needs,” he says.

“At the same time, productionising such models requires the application of good software engineering practices, to ensure the system meets its requirements for scalability, performance, availability and maintainability. Bringing these skills together empowers AI engineers to develop innovative approaches, optimise performance and unlock the full potential of AI technologies.”

How to get started on a career in AI

If you are thinking of a career in AI, you should first understand why you want to enter the field and what you’re interested in, says Ozzy Johnson, director of solutions engineering at Nvidia. Topics of interest can range from applied AI to the ethics of AI.

Education-wise, you can start building your AI skills through numerous MOOCs (massive open online courses) and degree courses. Hands-on experience can also be very beneficial, especially when working with AI experts from diverse backgrounds.

  • Start with networking and mentorship: You want to find people you admire and walk the path you want to be on over the next five years.
  • Get experience: The best way to learn is by doing. Don’t do it alone. If you can, grab your friends or colleagues, maybe start a study group, create a curriculum and meet once a week – it’s much more fun that way.
  • Develop soft skills: Learning and practicing public speaking. Practice talking about technical topics to non-technical audiences.
  • Define your why: Find a reason; something that drives you to stay motivated on your journey.

Besides technical skills, it is also key for AI engineers to have critical thinking and problem-solving skills, business acumen, as well as interpersonal skills such as communication and collaboration, says AWS’s Sklar.

This will enable them to look at multiple variables to determine the best course of action, and work with internal stakeholders and project teams quickly to support their organisation’s business goals, he adds.

Those who are looking to courses to kickstart their AI careers can turn to online materials and courses, such as Stanford University’s ML course on Coursera and the AI programme by the University of Helsinki.

Most cutting-edge research in AI/ML is also published for free in, an open-access repository, making it easy to keep up to date with the latest research trends. To learn practical skills and to test your skills against other AI engineers, Tuomola recommends trying out competitions at Kaggle, an online community for AI practitioners.

There are also AI/ML engineering courses provided by different cloud providers. AWS, for example, offers over 70 free digital training courses covering AWS ML services and solutions, with content ranging from foundational topics such as ML essentials for business and technical decision-makers, to advanced topics such as building ML applications.

“When you think of careers in AI, you probably want to think less about coding, AI research, data science and engineering, and more about consumption”
Allan Waddell, Kablamo

Budding AI professionals can also consider AWS’s ML Learning Plan designed to help AI engineers integrate AI and ML into tools and applications and prepare for the AWS Certified Machine Learning – Specialty certification examination.

Nvidia, whose GPUs are frequently used to crunch AI workloads, has training programmes to meet the needs of data scientists, application builders, creators, technical artists, researchers and IT administrators, covering the skills needed to architect, build, deploy, operate and support AI-based solutions and platforms.

But how important are certifications in one’s AI career? Salesforce’s Barfield contends that AI professionals should focus on the proficiencies they’ve gained through certifications, rather than the certification itself. “Gaining industry knowledge is also important, as it helps AI professionals better contextualise and understand the real-world applications and benefits of AI,” he adds.

Tuomola notes that certifications should not be seen as a substitute for practical experience. Instead, real-world experience – working on challenging projects and solving complex business problems – is the most valuable asset one can possess in the field of AI. “While certifications provide a foundation and enhance your skills, it is your ability to innovate, think critically, and apply your knowledge in practical settings that sets you apart.”

Making AI consumable

With AI capabilities increasingly being democratised through cloud services, the AI skills in greatest demand may not necessarily be technical in nature, contends Allan Waddell, co-CEO of Kablamo, an Australian technology services firm.

“When you think of careers in AI, you probably want to think less about coding, AI research, data science and engineering, and more about consumption,” says Waddell, who has a computing degree. “If I was doing my degree again, and doing AI, I’d be thinking double degrees in law and ethics. I’d be really resistant to going into a siloed AI degree – I’d maybe do a post-grad in it.”

And he explains why: “There won’t be a huge need for AI technology specialists, but there will be a big need for harnessing how it is applied. There are going to be very few people working on the optimisation of AI, especially when AI starts doing this optimisation itself, so I’d be looking at the application of AI.

“I’d be looking at digital product development using AI, making the complex simple. Consumption of AI is what matters now. How do you make AI consumable to your customers? It might be that user experience might be more important now than anything else – build the bridge from generative AI to what a business actually needs.”

On future-proofing one’s AI career, Waddell does not think it’s about future-proofing the current roles in AI, especially those that are highly specific and technical in nature. “It’s about how you stay general enough so you can pivot. You don’t want to get so specific now that there’s no way to get out.”

“Currently, AI is basically just another opinion, a probability engine generating an outcome. Gross replacement of careers isn’t happening yet and probably won’t for a while – we’re in a hype cycle around what AI is imminently capable of.

“Think of self-driving cars and all the money that’s been poured into that – there’s an almost infinite number of edge cases you have to train for, and we are still far from getting it right. The biggest risk around AI – and any specific careers in it – is the threat of premature regulation that stifles its growth and limits our options.”

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