Pivoting your developer career into AI? Here's what to know

Career opportunities in AI

Devs seeking to expand into AI have several career options. Sandhya Sridharan, global head of engineering platforms and experience at JPMorgan Chase & Co., highlights the following options:

Machine learning. Engineers focus on building out ML models and algorithms designed to make predictions and automate complex processes.

Data science. Those with software engineering skills are well-positioned to apply algorithms to analyze and interpret data sets to acquire the information needed to make data-driven decisions.

AI research. AI researchers explore how to build foundational models, as well as how to apply and advance the technology across industries.

App development. Examples include developing chatbots based on open-source models and deep learning algorithms.

Product management. While this isn't an engineering position per se, it's a necessary component of AI tech development. Whether a dev builds models or algorithms, or gathers insights and interprets large data sets, those specialized applications require product management that engages with consumers or end users to inform further advancements in AI technology, Sridharan explained.

AI consulting. Those with a solid foundation in AI can consult companies on how to use AI to accelerate processes or provide innovative solutions, Sridharan said.

The necessary skills to work with AI

According to Sridharan, working in the AI space requires the following skills and abilities:

Sandhya Sridharan,
global head of
engineering platforms
and experience at
JPMorgan Chase & Co.

• A solid understanding of the software development process.
• Strong software engineering skills.
• Competency in programming languages such as Java and Python.
• Great communication skills.
• The ability to collaborate effectively.

Analytical skills are especially important to developers looking to pivot to an AI-based role, said Ramprakash Ramamoorthy, head of AI research at ManageEngine, an IT management software development company headquartered in Del Valle, Texas. At his organization, the hiring process for data scientists and AI engineers includes an assessment of engineering and coding skills, as well as an interviewing round that focuses on how analytical candidates are.

"As an engineer, the first thing is to have a very analytical mindset," Ramamoorthy said. "That is what differentiates an ordinary software engineer from a software engineer who is ready to get into the AI world."

The challenges of working in the AI space

Data is the main driver behind AI, so access to large, high-quality data sets is one of the main issues to solve in this space.

"Historically, structured data is a little bit more under control with relational databases and all of that," Sridharan said. Unstructured data, such as that generated by social media networks, has varying quality. Lower quality data might contain biases which AI will replicate.

"That's one of the big challenges that we face when we work with AI," she said.

Security is another challenge. Organizations might rely on less expensive, open source AI models which might contain certain vulnerabilities that compromise security and privacy, Sridharan noted. Moreover, it's unclear how some AI models were built, so it can be difficult to ascertain how secure they really are.

The sheer speed of AI technology advancement also adds to the list of challenges. "Because the technology is so rapidly evolving, it's been hard to keep up," Sridharan said. "We really need to come up with creative ways to manage these challenges."

The most rewarding aspects of working in AI

While the challenges of working in AI are significant, so are the rewards.

One of the big pluses of an AI-focused career is that the tech helps developers and engineers focus on the more interesting parts of their jobs, Sridharan said. AI, and particularly generative AI, will automate the necessary but less fulfilling grunt work, and free up devs and engineers for more meaningful work to solve complex problems.

"Definitely the reward for software engineers is the reduction or elimination of those repeatable, mundane tasks," she said.

AI also helps jump-start other projects, by serving up a few initial lines of code and then letting humans take over.

Ramprakash Ramamoorthy,
head of AI research
at ManageEngine

"What I tell my team is: Never end with just what the AI gave you, but start with what the AI is going to give you," Ramamoorthy explained. "Take advantage of it. Start with it, and [then] add your secret sauces."

With such high demand for AI talent, there is a large pool of opportunities to choose from, as well as attractive compensation packages, Sridharan pointed out.

Above all, AI offers the chance to make a difference. "The biggest reward for most software engineers is [harnessing creativity] to build impactful solutions," Sridharan said. "[That] far outweighs compensation or any kind of talent acquisition reward."

Determining an organization's view of the potential for AI

Much of the hype around AI is focused on how the technology will eliminate jobs, including those in software development and engineering. Both Sridharan and Ramamoorthy agree that while AI can speed up the coding process, the industry needs devs and engineers to solve more complicated tasks and goals.

"Will it replace you as a programmer? No," Ramamoorthy argued. "It's going to augment your productivity."

When selecting which opportunities to pursue, it's wise to gain insight into how the company you're targeting views AI. Does the business focus on how to apply AI to reduce its workforce, including those in software development and engineering? Or is the company exploring AI deployment to strengthen its workforce?

"What's very important is: How does an organization foster a culture of innovation and creativity, whether it's AI or not?" Sridharan said. Organizations that prioritize creativity and innovation will seek to accomplish, or have already accomplished, initiatives such as the following:

• Develop a framework for using AI responsibly.
• Set ethical standards for AI use.
• Be transparent about how algorithms and models are developed and used.
• Follow security and compliance best practices.

"You don't start with the hypothesis that you need fewer engineers; you start with the hypothesis that your engineers are going to get faster," Sridharan said. "At the end of the day, most of us want to generate value for our customers, and that's the premise that most companies with the right culture will use when they use AI."

Carolyn Heinze is a Paris-based freelance writer. She covers several technology and business areas, including HR software and sustainability.