Dive Brief:
- Enterprise demand for AI and machine learning engineering roles has grown steadily in the last 10 years, according to a SignalFire analysis published Wednesday. The company analyzed data from 600 million employees spanning two decades through its Beacon platform.
- Specialized roles in AI and ML grew 2,700% since 2014, according to the report. Comparatively, DevOps and cloud engineering roles increased by about 200% in the same timeframe.
- Given high enterprise demand for AI/ML skills, technologists in these two categories show some of the lowest five-year retention rates, compared to more stable categories like software engineers, SignalFire found.
Dive Insight:
Under pressure to deliver on AI outcomes, enterprise leaders are working to overcome several barriers, including a lack of qualified talent.
Businesses with the full set of resources to deploy AI at scale — and reap its benefits — make up a slim minority. Just 2% of organizations possess the talent, data and technology to launch effective AI solutions, according to Infosys.
The pressure to fill open roles in AI has pushed up compensation packages for experienced AI engineers. Technologists with an AI background also feel more confident in their talent market prospects.
“For engineers interested in upward mobility, one of the most important factors driving career mobility is their field of specialization,” SignalFire said in the report. “Different engineering disciplines lend themselves better to rapid upward movement.”
AI engineers enjoy a short path toward management roles, moving up the leadership ladder in less than three years, on average.
To retain top talent, CIOs and other leaders should work to keep engineers engaged, especially once they reached the two- or three-year mark on the job, according to Jarod Reyes, head of developer community at SignalFire and author of the report.
“They should look for interesting technical challenges and ways to help engineers double down on a specialty, like leading a project,” Reyes said.
