How to Become an AI Developer: A Career Guide

The newly released Preparing for a Career as an AI Developer guide from the IEEE Computer Society argues that the most durable path to artificial intelligence jobs is not defined by mastering any single tool or model. Instead, it depends on cultivating a balanced mix of technical fundamentals and human-centered skills—capabilities that machines are unlikely to replace.

AI is reshaping the job market faster than most academic programs and employers can keep up with, according to the guide. AI systems now can analyze cybercrime, predict equipment failures in manufacturing, and generate text, code, and images at scale, leading to mass layoffs across much of the technology sector. It has unsettled recent graduates about to enter the job market as well as early-career professionals.

Yet the demand for AI expertise remains strong in the banking, health care, retail, and pharmaceutical industries, whose businesses are racing to deploy generative AI tools to improve productivity and decision-making—and keep up with the competition.

The uneven landscape leaves many observers confused about how best to prepare for a career in a field that is redefining itself. Addressing that uncertainty is the focus of the guide, which was written by San Murugesan and Rodica Neamtu.

Murugesan, an IEEE life senior member, is an adjunct professor at Western Sydney University, in Penrith, Australia. Neamtu, an IEEE member, is a professor of teaching and a data-mining researcher at Worcester Polytechnic Institute, in Massachusetts.

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

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

Why AI careers are being redefined

AI systems perform tasks that once required human intelligence. What distinguishes the current situation from when AI was introduced, the authors say, is not just improved performance but also expanded scope. Pattern recognition, reasoning, optimization, and machine learning are now used across nearly every sector of the economy.

Although automation is expected to reduce the number of human roles in production, office support, customer service, and related fields, demand is rising for people who can design, guide, and integrate AI systems, Murugesan and Neamtu write.

The guide cites surveys of executives about AI’s effect on their hiring and retention strategies, including those conducted by McKinsey & Co. The reports show staffing shortages in advanced IT and data analytics, as well as applicants’ insufficient critical thinking and creativity: skills that are difficult to automate.

The authors frame the mismatch as an opportunity for graduates and early-career professionals to prepare strategically, focusing on capabilities that are likely to remain relevant as AI tools evolve.

Developing complementary skills

The strategic approach aligns with advice from Neil Thompson, director of FutureTech research at MIT’s Computer Science and Artificial Intelligence Laboratory, who was quoted in the guide. Thompson encourages workers to develop skills that complement AI rather than compete with it.

“When we see rapid technological progress like this, workers should focus on skills and occupations that apply AI to adjacent domains,” he says. “Applying AI in science, in particular, has enormous potential right now and the capacity to unlock significant benefits for humanity.”

The technical foundation still matters

Adaptability, the guide stresses, is not a substitute for technical rigor. A viable AI career still requires a strong foundation in data, machine learning, and computing infrastructure.

Core knowledge areas include data structures, large-scale data handling, and tools for data manipulation and analysis, the authors say.

Foundational machine-learning concepts, such as supervised and unsupervised learning, neural networks, and reinforcement learning, remain essential, they say.

Because many AI systems depend on scalable computing, familiarity with cloud platforms such as Amazon Web Services, Google Cloud, and Microsoft Azure is important, according to the guide’s authors.

Mathematics underpins all of it. Linear algebra, calculus, and probabilities form the basis of most AI algorithms.

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

From algorithms to frameworks

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

“When we see rapid technological progress like this, workers should focus on skills and occupations that apply AI to adjacent domains. —Neil Thompson, MIT

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

The authors emphasize that such tools matter less as résumé keywords than as vehicles for understanding how models behave within real-world constraints.

Soft skills as career insurance

Because AI projects often involve ambiguous problems and interdisciplinary teams, soft skills play an increasingly central role, according to the guide. Critical thinking and problem-solving are essential, but communication has become more important, the authors say. Many AI professionals must explain system behavior, limitations, and risks to nontechnical stakeholders.

Neamtu describes communication and contextual thinking as timeless skills that grow more valuable as automation expands, particularly when paired with leadership, resilience, and a commitment to continuous learning.

Murugesan says technical depth must be matched with the ability to collaborate and adapt.

Experience before titles

The guide recommends that students consider work on research projects in college, as well as paid internships, for exposure to real AI workflows and job roles with hands-on experience.

Building an AI project portfolio is critical. Open-source repositories on platforms such as GitHub allow newcomers to demonstrate applied skills including work on AI security, bias mitigation, and deepfake detection. The guide recommends staying current by reading academic papers, taking courses, and attending conferences. Doing so can help students get a solid grounding in the basics and remain relevant in a fast-moving field after beginning their career.

Entry-level roles that open doors

Common starting positions include AI research assistant, junior machine-learning engineer, and junior data analyst. The roles typically combine support tasks with opportunities to help develop models, preprocess data, and communicate results through reports and visualizations, according to the guide.

Each starting point reinforces the guide’s central message: AI careers are built through collaboration and learning, not merely through isolated technical brilliance.

Curiosity as a long-term strategy

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

“Always be curious,” he says. “Learn from failure. Mistakes and setbacks are part of the journey. Embrace them and persist.”

Neamtu echoes that perspective, noting that AI is likely to affect nearly every profession, making passion for one’s work and compatibility with organizational aims more important than chasing the latest technology trend.

In a field where today’s tools can become obsolete in a year, the guide’s core argument is simple: The most future-proof AI career is built not on what you know now but on how well you continue learning when things change.