For tech professionals, artificial intelligence (AI) offers a galaxy of professional possibilities. Eventually, most platforms and apps will integrate some form of AI. For many who work in tech, this will mean learning new skills or programming languages, while others will need to figure out how to repurpose their existing knowledge to fit this new paradigm.
To discover what skills matter most—and whether those flashy AI certification courses mean anything for hiring managers—we spoke with experts to learn more about AI's most relevant skills and languages.
What are 3-5 skills every developer/engineer should master before utilizing AI?
Rahul Gulati, founder and chief strategist at GyanDevign Tech Services, says you should have mastery of machine learning fundamentals, data manipulation and processing, algorithm optimization, cloud services, and ethics before using AI.
“One cannot develop AI if he or she lacks some basic knowledge of the learning models such as supervised learning, unsupervised learning, or reinforcement learning,” Gulati notes, adding: “Basic algorithms must be well understood and their characteristics must be well known because machine intelligence models are often highly optimized for speed, precision, and other parameters.
“The realization of most of the AI applications demands large computational resources. It is in system software and cloud platforms like AWS, Google Cloud, or Azure. Additionally, they must understand how to distribute computational loads utilizing dispersed computing methodologies for handling AI workloads.”
Crown Chen, software developer at Acadia Healthcare, tells Dice you should have “a strong understanding of machine learning algorithms (e.g., supervised/unsupervised learning).
Proficiency in Python, especially with AI libraries like TensorFlow and PyTorch, experience with data analysis and preprocessing skills to clean and prepare data for models, knowledge of cloud computing platforms like AWS or Google Cloud for AI deployment, and a solid grasp of probability and statistics, which are crucial for AI and model evaluation.”
What are three skills/programming languages that don’t matter when using AI?
When starting with AI, it can be easy to follow the wrong path(s) and waste time learning things that won’t matter. Gulati says to avoid older programming languages like Fortran and COBOL: “Though these languages might still be used in specific older tools, the most recent developments in AI technology involve the use of Python, R, and Julia.”
Chen reminds us that front-end languages like HTML and CSS won’t matter when learning AI and agrees that COBOL is largely irrelevant to AI. Gulati adds that it’s just as important to not delve too deeply into mastering app interfacing for AI: “Artificial intelligence does not center on graphic design and web interface concepts.”
Do certifications for AI matter when hiring engineers or developers?
Certifications can help you stand out, but some aren’t worth it unless an employer specifically calls for one. It can signal to hiring managers that you’ve done the diligence to learn, but there are relatively few credentialed certification courses for AI at this point; that should change in coming years.
“Certifications can help show initiative but are not a dealbreaker,” says Chen. “Hands-on experience with real-world projects is far more important when assessing candidates.”
Again, with AI so new, certifications aren’t yet helpful in showcasing your skill set. “There are no shortcuts, and having practical experience and a record of implementing AI-related solutions are a thousand times more useful,” Gulati notes. “While certifications are really helpful, they should not be used as primary criteria for hiring. I value candidates with real-life items/Implementations/engaging with artificial intelligence rather than the most valuable certificates.”
What are the right (and wrong) ways to use AI?
“Use AI for solving real problems, ensuring model explainability, and continuously refining models based on performance,” Chen says. “Over-relying on AI without proper validation, using AI as a ‘black box’ without understanding the underlying algorithms, or ignoring ethical implications is wrong.”
“The wrong approach would be to approach AI as an abstract solution in which the developers do not have deep insights into its operations,” Gulati adds. “One of the pitfalls is to apply the technology in areas where other techniques can work just as well, which complicates the problem without any additional advantage.
“AI should be employed where the abilities of humans will be enhanced, repetitive functions will be eased, and decision-making enhanced. There are certain recommendations that the developers should use in creating the AI systems, including the use of transparent, explainable, and needs-based AI systems.”
Conclusion
Focus on modern programming languages and their relative use for AI. Tinker with GitHub repos, side projects, and blogging about your experience. There’s no shortcut to mastering AI, so you’ll have to dig in and get your hands dirty.
If you want to find a job where you can work on AI, learn the intricacies of machine learning and large language modeling. These are the core frameworks for AI and will prove helpful for long-term success.
