Tips for building a career as an AI developer

The IEEE Computer Society’s newly released Guide to Preparing for a Career as an AI Developer argues that the most durable path to a career in artificial intelligence is not defined by mastering a single tool or model. Instead, it depends on developing a balanced mix of technical fundamentals and human-centered skills, capabilities that are unlikely to be replaced by machines.

According to the guide, AI is reshaping the job market faster than most academic programs and employers can keep up. AI systems can now analyze cybercrime, predict equipment failures in manufacturing, and generate text, code, and images at scale, leading to mass layoffs in many technology sectors. This is worrying new graduates and early career professionals looking to enter the job market.

However, demand for AI expertise remains high in banking, healthcare, retail, and pharmaceutical industries, which are rushing to adopt generative AI tools to improve productivity, decision-making, and stay competitive.

The uneven landscape has left many observers confused about how best to prepare for a career in a field that is redefining itself. Dealing with this uncertainty is the focus of this guide written by San Murugesan and Rodhika Neamthu.

Mr. Murugesan is an IEEE Life Senior Member and an Adjunct Professor at the University of Western Sydney, Penrith, Australia. IEEE member Neamtu is a professor of education and data mining researcher at Worcester Polytechnic Institute in Massachusetts.

A 24-page downloadable PDF outlines what aspiring AI professionals should focus on, which skills are most likely to remain most valuable amid rapid automation, and why careers in AI increasingly emphasize applying algorithms thoughtfully across domains rather than building them in isolation.

This guide emphasizes adaptability as the defining requirement for entering the field, rather than fluency in a particular programming language or framework.

Why careers in AI are being redefined

AI systems perform tasks that once required human intelligence. What’s different now than when AI was first introduced, the authors say, is not just improved performance, but expanded scope. Pattern recognition, inference, optimization, and machine learning are now used in almost every area of ​​the economy.

While automation is expected to reduce the number of human roles in production, office support, customer service, and related fields, the demand for talent who can design, guide, and integrate AI systems will increase, Murugesan and Neamthu write.

The guide cites surveys of executives on the impact of AI on their companies’ recruitment and retention strategies, including one conducted by McKinsey & Company, which show a shortage of advanced IT and data analytics talent, as well as a lack of critical thinking and creativity among applicants, skills that are difficult to automate.

The authors see this discrepancy as an opportunity for graduates and early-career professionals to strategically prepare by focusing on capabilities that are likely to remain relevant as AI tools evolve.

Developing complementary skills

This strategic approach is consistent with advice from Neil Thompson, Director of FutureTech Research at the MIT Computer Science and Artificial Intelligence Laboratory, cited in the guide. Thompson encourages employees to develop skills that complement AI rather than compete with it.

“When we see such rapid technological advances, workers need to focus on skills and occupations that apply AI to adjacent areas,” he says. “The application of AI, especially in the scientific field, currently has great potential and could bring great benefits to humanity.”

Technical foundations remain important

The guide emphasizes that adaptability is not a substitute for technical rigor. A career in AI still requires a strong foundation of data, machine learning, and computing infrastructure.

According to the authors, core knowledge areas include data structures, large-scale data processing, and tools for data manipulation and analysis.

They say basic machine learning concepts such as supervised and unsupervised learning, neural networks, and reinforcement learning remain essential.

According to the authors of this guide, many AI systems rely on scalable computing, so it’s important to be familiar with cloud platforms such as Amazon Web Services, Google Cloud, and Microsoft Azure.

Mathematics supports everything. Linear algebra, calculus, and probability form the basis of most AI algorithms.

Python has emerged as the leading language for building and experimenting with models.

From algorithms to frameworks

The authors emphasize the value of hands-on experience with widely used development frameworks. PyTorch, developed by Meta AI, is commonly used in academia and industry for prototyping deep learning models. Scikit-learn provides open-source tools for classification, regression, and clustering within the Python ecosystem.

“With such rapid technological advancements, workers need to focus on skills and occupations that apply AI to adjacent areas. —Neil Thompson, MIT

TensorFlow is a software library for machine learning and AI created by Google that supports building and deploying machine learning systems at multiple levels of abstraction.

The authors emphasize that such tools are important less as keywords on a resume and more as a way to understand how a model behaves within real-world constraints.

Soft skills as career insurance

According to the guide, soft skills are playing an increasingly central role because AI projects often involve ambiguous problems and multidisciplinary teams. Critical thinking and problem solving are essential, but communication has become even more important, the authors say. Many AI professionals need to explain system behavior, limitations, and risks to non-technical stakeholders.

Neamtu said communication and situational thinking are timeless skills that will become more valuable as automation expands, especially when combined with leadership, resilience, and a commitment to continuous learning.

Murugesan says there needs to be a match between technical depth and the ability to collaborate and adapt.

Experience before title

The guide recommends that students consider working on a research project or taking a paid internship at university to gain exposure to real-world AI workflows and roles with hands-on experience.

It’s important to build a portfolio of AI projects. Open source repositories on platforms like GitHub allow newcomers to demonstrate applied skills such as AI security, bias reduction, and deepfake detection. This guide recommends staying up-to-date by reading academic papers, taking courses, and attending conferences. This allows students to gain a solid foundation and remain relevant in a rapidly changing field once they begin their careers.

Entry-level role that opens doors

Common starting positions include AI Research Assistant, Junior Machine Learning Engineer, and Junior Data Analyst. According to the guide, this role typically combines support tasks with opportunities to help develop models, preprocess data, and communicate results through reporting and visualization.

Each starting point reinforces the core message of this guide. This means that careers in AI are built through collaboration and learning, not just through isolated technical talent.

Curiosity as a long-term strategy

Murugesan encourages aspiring AI professionals to embrace continuous learning, seek mentorship, and treat mistakes as part of the learning process.

“Always stay curious,” he says. “Learn from your mistakes. Mistakes and setbacks are part of the journey. Embrace them and persevere.”

Neamtu echoed that view, noting that AI is likely to impact nearly every profession, and that passion for your work and compatibility with your organization’s goals will be more important than following the latest technology trends.

In a field where current tools may be obsolete in a year’s time, the guide’s central argument is simple. In other words, the most promising AI careers aren’t built on what you know now, but on how well you can continue to learn as circumstances change.