Artificial intelligence (also known as AI, as you know it) is shaping the next phase of technology for everyone. Developers and engineers are already starting to discover where and how AI fits into the technology stack. Companies like Apple, Microsoft, and Google are weaving AI models across a variety of platforms and services, pushing users deeper into their ecosystems.
For technology professionals, AI can present exciting opportunities. Technologies like machine learning and natural language processing have been around for some time, but a new generation of AI tools and services can now use them in interesting ways that present new paths to success and career advancement. In other words, if deployed and used correctly, AI can help technical professionals work faster and smarter than ever before.
But those interested in AI may need to understand where to start. Keeping up with the rapidly evolving AI ecosystem will take a lot of effort. Which programming languages are required? Will an AI certification help you get a job? Should you contribute to an open source AI repository to stand out?
We spoke to experienced technologists to better understand where you should focus your efforts if you want to leverage AI in 2025 and beyond.
If someone is interested in becoming an AI developer, what should they focus on in school?
Koushik Sundar, vice president of Citibank, advises students to focus on the basics for admission. “It’s important to build a strong foundation, especially in mathematics, probability, and statistics. AI is a broad field that includes deep learning, neural networks, and GenAI. What is currently evolving are hybrid solutions that combine multiple tools and algorithms to develop AI agents and tools. Building a strong foundation is important to gain a holistic view and understand how things work behind the scenes.”
charlie clarkeformer senior software engineer at Squarespace and founder of Liinks, agrees.
“I think it’s important to really build on three skills: math, algorithms, and curiosity about the unknown,” Clark says. “At the heart of AI is turning data into insight, which means you need a deep understanding of linear algebra, statistics, and calculus. Rather than memorizing formulas, you need to know how a model is thinking internally. This, combined with data structures and algorithms, allows you to efficiently build systems that process data at scale. But beyond textbooks,‘It’s about cultivating curiosity. Take time to experiment with projects outside your comfort zone, whether it’s training a small neural network to recognize cats or building a simple chatbot for your friends. AI is not‘a straight path, it‘It is a combination of discipline and creative exploration. ”
Are there any AI certifications that can help experienced developers/engineers transition into AI?
“Certifications are helpful, but I‘We encourage you to look at them as tools rather than tickets,” Clark says.‘■ A TensorFlow Developer Certification or AWS Certified Machine Learning Expertise shows you understand the tool, but how you use it is important. Think of it this way. Owning a hammer means‘It doesn’t make you a carpenter. You need to know when and how to use it. For those thinking of pivoting, I‘I recommend Andrew Ng‘Coursera’s deep learning specialization — it‘It’s not just a lecture. that‘This is a mindset shift that helps you think about real-world AI applications rather than abstract theory. ”
“Google, Microsoft, and IBM offer certifications, and there are crash courses like MIT,” Sander points out. “Organizations such as the Singapore Computer Society also offer courses that allow you to earn certifications after passing an assessment.If you are focused on learning, there are many open courses available on platforms such as YouTube and Udemy.
Many companies at the forefront of AI development offer certifications, but the jobs themselves do not require AI certification (yet). Unlike platforms like Salesforce, AI certifications are currently not considered evidence of a solid understanding of the technology or specific model.
What are the most important programming languages for developers/engineers interested in AI?
Sander says that good old Python is a great start: “Python is a good choice because it has a variety of libraries that simplify the implementation of core mathematical, probability, and statistical concepts. These libraries are also relatively efficient compared to other programming languages.”
“From a work perspective, it is useful to know additional languages such as Java, as many industry applications use hybrid solutions, as business functions may be written in different programming languages.‘It is beneficial to know more than one language in order to understand and work with both languages. ”
“For me, Python is the universal translator for AI. Its libraries TensorFlow, PyTorch, and Scikit-Learn simplify complex processes and allow you to focus on logic rather than syntax. But if you want to dig deep into optimization or real-time applications, C++ becomes essential, especially when deploying models where milliseconds matter.”
“I also think Julia is a sleeper hit; its number-crunching speed could be the next big thing for those whose research is focused on critical roles.‘It’s about choosing the right tool for the problem, just like a chef knows when to use a paring knife and a kitchen knife. ”
What are some good open source AI repositories for developers looking to add to their portfolio?
Contributing to open source repositories is a great way to prove your knowledge and demonstrate your skills. “When it comes to open source AI, I believe quality is key over quantity,” Clark says. “hug face”‘Transformers are a treasure trove for you.‘Interest in natural language processing or NLP‘In addition to using pre-trained models, it is also important to understand how to adapt the model to your specific needs. fast.ai provides an amazing balance between deep learning theory and real-world applications. Train powerful yet easy-to-understand models.
“For those looking to showcase their data preprocessing skills, DVC (Data Version Control) helps you track datasets and models like a pro and bridges the gap between machine learning and data engineering.‘We don’t just provide code. that‘About contributing to the understanding of AI‘ecosystem. ”
Rather than chasing after a good AI repository, Sundar advises engaging with the repository of the underlying framework, such as TensorFlow. Demonstrating proficiency with platforms and frameworks that support AI may be uniquely valuable in the early stages of AI.
