Key Skills to Look for in an AI Developer

AI developers are in high demand. Here's how to separate talented pros from the swarm of wannabes.

John Edwards, Technology Journalist & Author

June 12, 2024

5 Min Read
Big data engineer, computer programmer or software developer busy thinking
Panther Media GmbH via Alamy Stock Photo

Suddenly, it seems like everybody wants to be an AI developer. Yet only a select few individuals actually have the abilities necessary to design, develop, and implement enterprise-level AI models and systems. 

Are you searching for an AI expert staff member or consultant? Here, via email interviews with experts, are the eight attributes you need to look for in any in-house or external AI developer candidate. 

1. A firm grasp of AI basics 

At the very least, every AI developer should have an understanding of generative AI and large language models (LLMs), says Sunil Kalra, head of data engineering at data analytics firm LatentView Analytics. Other fundamental concepts, he notes, include UX design principles, security and risk management, LLM lifecycle management, and fine-tuning LLMs. Familiarity with vector databases and their governance is also important, while experience with multiple generative AI service offerings adds value. "Additionally, having business knowledge is crucial for effective prompt engineering, as it enables the translation of business objectives and domain-specific expertise," Kalra says. "This combination of technical and business expertise ensures successful AI implementation." 

2. Programming proficiency 

Candidate skills should include programming proficiency in languages such as Python and R, expertise in machine learning algorithms, and analytical capabilities, as well as domain knowledge and problem-solving skills, advises Kenny Brown, managing director at business advisory firm Deloitte Consulting. The combination of domain knowledge and a heightened awareness of unconscious biases is crucial for AI developers, especially in today's context. The age-old adage "garbage In, garbage out" still holds true, albeit in a more nuanced manner, and it’s necessary to implement guardrails to mitigate unintended outcomes.

Related:Will Generative AI Replace Developers?

3. Strong systems knowledge 

Ensuring that an AI developer has solid foundational knowledge of AI systems and the frameworks required to build them is key, observes Eric Velte, CTO at ASRC Federal, a civilian, defense, and intelligence agencies consulting firm. "AI development is different than traditional software development, and specialized skills are required to reap the maximum benefit," he explains. Velte warns that with current technology it's too easy to trust a model and its results. "This is a danger that needs to be overcome with statistical methods and a healthy skepticism." Hiring developers with systems knowledge will not only create greater efficiencies, but also help secure organizational data. 

Related:How Developers of All Skill Levels Can Best Leverage AI

4. A commitment to data stewardship 

Beyond technical skills, candidates should possess strong data stewardship attributes, says Andrew Fedorchek, CTO, technology, data, and analytics at Mastercard. Stewardship includes "ensuring security and privacy, maintaining transparency and control over data usage," he notes. It also means "embracing diversity for inclusive and equitable outcomes, upholding integrity to minimize biases and unintended consequences, fostering innovation for enhanced data usage benefits, and leveraging data for positive social impact." 

5. A strong belief in AI ethics 

AI developers require skills that extend far beyond technical proficiency, observes Nick Elsberry, leader in software technology consulting at digital transformation specialist Xebia. "In the early stages of widespread AI adoption, recruiting talent possessing a commitment to ethical practices is paramount." He adds that developers should also commit themselves to sustainable efficiency to ensure that AI systems are developed and deployed responsibly while safeguarding against privacy breaches, biases, plagiarism, and other common perils. Elsberry recommends that AI developers should also align themselves with enterprise ethics and governance practices. 

Related:IBM Talks Bridging the AI Trust Gap with Developers

6. Mathematics and statistics mastery 

Understanding concepts such as linear algebra, probability, calculus, and statistics is crucial for algorithm development, data analysis, and model building, says Nate Dow, director of technology at IT services firm BairesDev. Machine learning and deep learning knowledge is also important, as familiarity with various machine learning algorithms. 

7. Solid data management skills 

Ever since the "big data" movement got underway several years ago, many IT leaders have observed that most of the work involves data collection and cleaning. AI doesn't change that view, says Mike Loukides, vice president of emerging technology content at educational publishing and services firm O’Reilly Media. It's not a matter of taking a foundation model that has all of the world's knowledge built-in and setting that application loose on the problem, he says. "You're going to need to collect data for fine tuning." An AI expert has to understand what that data means and what kinds of biases are built into the data. 

8. Strong communication abilities 

Beyond technical skills, a candidate should be able to explain concepts and results to business stakeholders in clear and simple terms, advises Jayaprakash Nair, head of analytics at data and digital engineering services company Altimetrik. "AI explainability poses a major challenge in the industry," he notes. Business leaders shouldn't feel confused or misled by cryptic terms or jargon. 

About the Author(s)

John Edwards

Technology Journalist & Author

John Edwards is a veteran business technology journalist. His work has appeared in The New York Times, The Washington Post, and numerous business and technology publications, including Computerworld, CFO Magazine, IBM Data Management Magazine, RFID Journal, and Electronic Design. He has also written columns for The Economist's Business Intelligence Unit and PricewaterhouseCoopers' Communications Direct. John has authored several books on business technology topics. His work began appearing online as early as 1983. Throughout the 1980s and 90s, he wrote daily news and feature articles for both the CompuServe and Prodigy online services. His "Behind the Screens" commentaries made him the world's first known professional blogger.

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