Working on AI: What does that truly mean?

Welcome to the August edition of Tech Career Decoded. This month, we're shifting our focus from working with AI to working on AI. While many of us are now familiar with using AI in our daily lives, we're pulling back the curtain to explore the builders: the engineers and developers who are creating, training, and deploying these powerful models. We'll dive into what it truly means to be on the front lines of AI development, examining the new roles emerging in the industry, and actionable advice from Michael Page experts for tech professionals looking to make the leap.

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Using AI vs. Building AI

If you work in or are entering tech, you know AI. The question is, do you understand the difference between using it and building it? Just so we're all on the same page, let's quickly break down the differences.

Using AI is what most of us do every day. You fire up a tool like GitHub Copilot and integrate it into your workflow. You're the consumer, leveraging a finished product to code faster or be more productive.

Working on AI is all the complex engineering and research that happens before a tool like Copilot can even suggest its first line of code. This is the world of designing neural network architectures, wrangling massive datasets, and creating the MLOps pipelines to train and deploy these models at scale. This work typically falls into three main categories:

• AI: This involves building predictive systems for tasks like classification or forecasting, such as a model that detects fraudulent credit card transactions.

• Generative AI (GenAI): This is about engineering models like LLMs that generate content by learning the underlying patterns in a dataset, like a tool that creates an image from a text description.

• Agentic AI: This involves creating systems that can reason, plan, and execute multi-step actions to achieve a complex goal. For example, an AI that takes a feature request, writes the corresponding code, and submits it for review.

But who exactly is behind this complex and cutting-edge work? Let's dive into the roles that are bringing these AI systems to life.

֎ What roles are responsible for building AI?

AI is not just changing the tech stack; it's reshaping the job market. While headlines often focus on job losses, the real story is about creation and change. A report from the World Economic Forum predicts that by 2025, AI will create 97 million new roles, far outpacing the 85 million it might eliminate. 

So, who are the people building AI? There’s no single job description, but the work is being done by a growing group of specialists. You’ll see common titles like Machine Learning Engineer, Data Engineer, and AI Software Engineer forming the core of many teams. But as companies get more serious about AI, the roles get more specific:

“We're also seeing traditional data roles, like Data Scientist and Data Engineer, now come with expectations around AI capabilities. More recently, I’ve seen titles like AI Product Manager or Prompt Engineer show up, particularly in companies that are further along in their AI journey.”

But most companies aren't building foundational AI models from the ground up. Instead, they're focused on building their own AI products by integrating and customising existing models like GPT, Gemini, and Claude. This has led to the growing importance of "AI-adjacent" roles:

“Many companies are adopting existing foundation models and tools, in turn leading to strong demand for AI-adjacent roles in data, cloud architecture, and integration. These are critical to any AI rollout and reflect the broader trend of organisations needing foundational capabilities before they can scale more advanced AI use. We’re seeing demand for candidates who have the skills to integrate AI into existing systems and business workflows, while ensuring data privacy, operational resilience, and long-term scalability.”

Now you know the roles that are responsible for building AI tools and products. The next question is, how do you start or switch to a career in AI? Let's find out!

4 tips on how to break into an AI career  

For many tech professionals, working on AI is quickly becoming one of the most valuable and strategic career paths available. But how do you transition into an AI role? Whether you're an experienced professional looking to pivot or a junior dev mapping out your future, the path isn't always obvious. Michael Page experts share actionable advice for tech professionals looking to build a career in the fast-growing field of AI.

“It's not just engineers and developers who are interested in transitioning into AI-related roles, strategy professionals are equally drawn to AI-first organisations. Additionally, Project Managers, Delivery Managers, and Product Managers are also increasingly looking to add AI-focused projects or products to their professional portfolio.”

1️⃣ Start from where you are, not from scratch 

The most common misconception is that you need to abandon your current expertise to enter the AI field. In reality, your existing knowledge is your biggest asset. AI is not just a standalone function, it’s becoming a layer across every part of a business. The first step is to identify where AI is already intersecting with your domain and build from there.

“I would encourage candidates exploring opportunities in AI to view this technology as an accelerant for opportunity rather than a standalone specialisation. Many organisations are adapting AI in every function including legal, HR, finance, IT and operations—and this will only continue to increase. Start by identifying where AI is already touching your area of expertise, then build out from there. You don’t need to become a data scientist overnight, but you do need to understand how AI will affect your decisions, your tools, and your stakeholders.”

Ronald Howson , Head of Page Consulting UK

2️⃣ Bridge the gap between technology and business value 

While technical skills are important, tech professionals who truly stand out are those who can connect AI capabilities to business impact. Companies are looking for people who can answer not just "How do we build this?" but "Why should we build this?" Learning to speak the language of strategy, optimisation, and value creation will set you apart. This blend of skills is what defines the most sought-after profiles:

“What’s interesting is the profile that’s emerging: not just engineers who understand AI technically, but professionals who can connect it to business impact, using AI to drive value, optimise processes, or support decision-making. I also hear a lot of concerns about AI replacing jobs, which is understandable, but I believe AI is more of an amplifier than a threat. The professionals who learn how to use AI as a tool will enhance their roles, not lose them.” 

Julia Riela , Senior Consultant Michael Page Technology, Brazil

3️⃣ Get hands-on experience and build your technical toolkit

Once you’ve framed the business problem, you need the technical expertise to help solve it. This is where focused upskilling comes in. The landscape is evolving fast, so a mindset of continuous learning is non-negotiable. Take online courses, work towards certifications from cloud providers (like AWS, Google Cloud, or Azure), and most importantly, build a portfolio. A public GitHub repository with projects, no matter how small, speaks volumes more than a certificate alone.

“Start by building a strong foundation in machine learning and Python, using hands-on tools like scikit-learn and TensorFlow. Leverage your existing tech experience—whether in software development, data, or DevOps—to move into adjacent roles such as MLOps or data engineering. Join AI communities, contribute to open-source projects, and continuously upskill by taking courses, reading papers, and experimenting with real-world datasets.” 

Damini Sarin , Manager at Michael Page India

4️⃣ Consider pursuing a formal degree

If you're just starting your career, it could be a good idea to enroll in undergraduate and master's degrees in AI, Machine Learning, and Data Science. These programs can provide a deep theoretical foundation that is invaluable, but they are by no means the only route. For the vast majority of professionals already in the industry, a combination of self-study, targeted certifications, and on-the-job experience remains the most effective path forward.

💡Final thoughts: The future of AI-tech jobs

As we've explored, working on AI is fundamentally reshaping the tech industry. But the changes we're seeing now are just the beginning. We are at a turning point where traditional job models are giving way to entirely new career paths. Soon, team rosters won't just include engineers and data scientists, they'll feature roles that sound like science fiction today, such as AI Ethicists, AI Personality Designers, and AI Trainers.

This evolution is about more than just new titles. It signals a deep shift in the skills that matter. Across all IT jobs, a new set of critical competencies is emerging, centred on AI literacy, data analytics, and rapid engineering. As models handle more routine tasks like basic coding and documentation maintenance, the high-value skills are becoming more strategic. Expertise in areas like responsible AI, LLM architecture, and AI ethics will define the next generation of tech leaders.

“We’re at the beginning of a significant shift in how work is structured and how organisations think about skills. AI won’t replace entire functions, but it will change what high performance and future job specs look like in almost every role. The organisations that succeed won’t just be the ones with the best models or the biggest budgets. They will be the ones that build the right human infrastructure around AI with people who understand the technology but also know how to apply it responsibly and at scale at all levels in the workforce.”

Ronald Howson , Head of Page Consulting UK

The path into an AI career is different for everyone. Some of you are already deep in the field, while others are just beginning to map out the journey. Your experience, no matter what stage you're at, is valuable. 

If you're interested in an AI career, what steps are you taking right now to make the leap? If you're already in an AI-focused role, what is the one piece of advice you would give to a fellow tech professional looking to break in? Share your story or your expertise in the comments!  

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The Michael Page Technology team