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- AI skills are in high demand as companies aim to leverage AI for competitive products.
- AI expertise can lead to lucrative roles in Big Tech or startups and substantial pay raises.
- Here are some of the key skills that employers are seeking for AI-related roles.
AI skills are in high demand in the job market as more companies seek to use the technology to compete with rivals and become more efficient.
Having AI expertise could also land you a position at a Big Tech giant, startups — or even get you a pay bump.
Nancy Xu, founder and CEO of AI recruitment company Moonhub, told Business Insider that her firm is seeing an uptick in demand for "technical generalists who can build AI applications, along with domain experts in several emerging areas of AI research, large language model training and fine-tuning, and machine learning infrastructure deployment."
She says some companies are going the extra mile to win AI talent. "We're seeing CEOs flying to candidates to close offers, significantly above-average sign-on and performance bonuses, new equity structures, customized benefits for individuals, and more," Xu said.
Iffi Wahla, CEO of global talent network Edge, said data scientists have been among the best-paid tech workers in recent years, partly because every business needs people who can understand and extract value from data.
Companies want to spread AI skills across their operations, so those with a background or training in techniques such as prompt engineering on generative AI will benefit from increased job opportunities and pay, Wahla added.
Here are some of the most sought-after skills that may help you land a well-paying job in AI.
Aswini Thota, director of data science at financial service firm USAA, told BI that when hiring data scientists and AI engineers, he assesses candidates based on three key areas: technical prowess, business acumen and communication, and innovation.
Technical knowledge
Thota says data scientists are expected to be well-versed in Python and R, the most popular programming languages for building AI models, while some companies use C++ and Java.
They're also expected to have a foundational knowledge of statistics, as well as machine learning algorithms and frameworks in Python or R.
"A vast majority of organizations rely on cloud technology to store, analyze, and build models, so a working knowledge of Amazon Web Services, Google Cloud Platform, Snowflake, Microsoft Azure, Databricks, and similar platforms has become increasingly important in recent years," Thota said.
Business acumen and communication
According to Thota, data scientists looking to land a job in AI should also have strong business acumen to grasp an organization's challenges and develop solutions. "Communication skills come into play when data scientists must explain the results and influence decision-makers to align with the technical approach they recommend."
Innovation
When recruiting for senior or lead roles, Thota says he looks for candidates who have the potential to lead with innovation. "Hiring candidates with an innovative mindset helps us anticipate and address potential challenges before they become issues and also develop groundbreaking solutions."
Flexibility and ongoing learning
Ram Srinivasan, a future of work leader at consulting firm JLL, says some of the most sought-after AI competencies include a combination of technical and soft skills.
They include having a "strong learning mindset and adaptability" because employers look for candidates who can quickly adopt new technologies and methods.
Problem-solving and teamwork
Srinivasan adds: "AI projects often involve complex challenges requiring innovative problem-solving skills. Collaborating effectively with diverse teams, including data scientists, project managers, and product developers, is also essential."
Ethical considerations
AI development poses ethical questions and risks that engineers and developers must navigate responsibly.
Identifying use cases
Srinivasan said tech workers should be able to spot potential AI applications across industries, assess their feasibility, and implement them effectively.
"This involves understanding various sectors, developing implementation strategies, managing organizational change, and measuring ROI. Skills in expanding successful AI pilots and facilitating user adoption are crucial."