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The New AI Team: 22 Roles You Haven’t Heard Of

AI jobs are exploding faster than you can say chatbot.

I nearly spilled my coffee the other morning when I saw a job listing for an AI Prompt Engineer offering a salary of Є 300,000 or more. A year ago, that job title sounded like something out of a sci-fi novel. Today, it’s genuine – companies are scrambling to fill positions that didn’t exist until recently. From “Model Manager” to “AI Ethicist,” a whole new cast of AI specialists is taking the stage, and it’s changing how tech teams build the future. 

Why is this happening now? In short, because AI went mainstream in a big way. Last year, everyone from your cousin to your CEO discovered ChatGPT and generative AI. Suddenly, every organization had an “AI strategy,” and they quickly realized their old teams weren’t enough. We used to think having a few data scientists and engineers on staff was cutting-edge; now it’s just the starting point. 67% of mature AI organizations are creating new AI roles, and 87% already have dedicated AI teams​. The talent landscape is shifting so fast that even Gartner has put out a chart mapping all the new roles emerging in AI projects. 

Gartner’s “New Roles for AI” chart lays it all out visually: a spectrum of roles orbiting the AI function, from core builders like Data Scientist and AI Developer (the familiar staples) to newcomers such as Prompt Engineer and AI Ethicist. This graphic shows how diverse an AI team can be. If it feels like alphabet soup, you’re not alone – just a couple of years ago, nobody had a title like Model Validator or AI Risk and Governance Specialist. Now, they’re becoming must-haves in any company serious about AI innovation.

From Data Scientists to Prompt Engineers: Meet the New AI Crew

Let me walk you through this new AI crew and how they compare to the old guard. I remember when data scientist was dubbed the “sexiest job of the 21st century,” and hiring one or two felt like winning the lottery. Those data scientists are still here (and still crucial), but a supporting cast of specialists now joins them. Think of it like a movie production: we used to have just directors and actors, and now we have CGI experts, sound designers, stunt coordinators – more roles to make a better film. The AI world is similar.

AI Crew Structure
  • Data Scientist (Traditional): The go-to AI role of the last decade, focused on analyzing data and developing models​. They’re like the lead actors, turning raw data into insights and predictions. But as AI projects grew, data scientists needed help – they no longer work solo.
  • Data Engineer (Traditional): The unsung hero who makes sure data is available and high-quality. Data engineers build and maintain the data pipelines that feed the AI models​. Five years ago, this role was already crucial for big data projects, and it’s just as critical now for AI. Without them, a data scientist might spend half their day just finding or cleaning data instead of building models.
  • ML Engineer (Traditional/Evolving): A hybrid between data science and software engineering, the machine learning engineer was introduced to take those models and implement them in code, ensuring they run efficiently in production. This role has been around for a few years, bridging the gap between the experimental work of data scientists and the reliable software that end-users expect.
  • Software Engineer (Traditional): The classic developer role isn’t going away. In AI projects, software engineers still build the applications and infrastructure around AI models. What’s changed is that they now collaborate closely with AI specialists to integrate complex models into products. If a data scientist builds a recommendation algorithm, a software engineer ensures it appears on your screen and doesn’t crash your app.

Now, onto the fun new job titles that have popped up thanks to the AI revolution:

  • Prompt Engineer (Emerging): Have you ever tried phrasing a question differently to get a better answer from ChatGPT? Prompt Engineers do that for a living. They design and tweak the questions or instructions given to AI models to get the best results. It sounds almost whimsical – like whispering secrets to a robot – but it’s become essential with the rise of generative AI. (And yes, some companies will pay big for this skill – one AI startup offered up to $375,000 for a prompt engineer​. This role didn’t exist pre-ChatGPT. Now it’s one of the hottest jobs, ensuring AI systems produce valid and reliable output instead of gibberish​.
  • Knowledge Engineer (Emerging): Do you remember the old-school AI that used expert systems? Well, the concept is back. A knowledge engineer takes what subject-matter experts know and translates it into a form that an AI system can use – think of building knowledge graphs or ontologies. They ensure the AI isn’t just making statistical guesses, but also aware of domain knowledge. In short, they translate expertise into a knowledge base that AI can draw on​. This role complements the data scientist by adding curated knowledge to pure data-driven approaches.
  • Model Manager (Emerging): Once you have dozens of AI models running, who keeps track of them? Enter the model manager. This person oversees the AI model lifecycle – from inception and training to deployment, updates, and eventually the retirement of models. It’s a bit like a product manager, but for AI models specifically. In the past, a data scientist might’ve informally done this between coding sessions. Now it’s someone’s full-time job to inventory models, version them, monitor their performance, and ensure the team reuses what works.
  • Model Validator (Emerging): Think of this as the quality assurance tester for AI. A model validator’s job is to rigorously test AI models before they go live, verifying their accuracy, fairness, and reliability​. In regulated industries (like finance or healthcare), this role is enormous – you don’t want a rogue model making bad loans or misdiagnosing patients. Traditionally, validation was left to data scientists or, occasionally, peer review. But as AI systems get more complex, having a dedicated AI model accuracy watchdog is becoming standard.
  • Model/MLOps Engineer (Emerging): You might hear the term “MLOps” (machine learning operations) a lot. It’s all about deploying and maintaining models in production. A Model/MLOps Engineer is the specialist who automates the pipeline from model development to deployment and monitoring. They ensure that what the data scientists build in the lab works 24/7 in the real world. This role is an evolution of the ML Engineer and DevOps roles, combining knowledge of machine learning and software deployment. Without them, many AI projects would stall at the prototype phase​.
  • AI Developer (Emerging/Hybrid): This title may sound generic, but it refers to a software developer who specializes in creating AI-powered applications. An AI developer might implement a new computer vision algorithm into an app or integrate an open-source model into your company’s platform. Gartner describes it as someone who builds and implements generative AI and “agentic” systems​. In other words, they’re coding the glue that brings AI into everyday software. In the past, software engineers would do this, but now the complexity of AI components has spawned a need for developers with AI-specific expertise.
  • Analytics Engineer (Emerging): This role sits between data engineering and data analysis. An analytics engineer focuses on transforming, cleaning, and organizing data in a way that’s ideal for analytics and AI models. They build pipelines and data products that deliver ready-to-use data for machine learning. Essentially, they build AI-driven analytics solutions​. In the past, a data engineer or a BI analyst might have done this as part of their job, but the rise of big data and AI has made it a specialized, full-time role in many teams.
  • AI Translator (Emerging): Officially called a Data & Analytics (D&A) and AI Translator, this is the bridge between the tech team and the business side. Not everyone speaks fluent “AI,” so translators step in to help define problems and requirements, and ensure that the data scientists solve the correct problems. They talk to business experts and data experts, ensuring nothing gets lost in translation. It’s a role Gartner highlighted as crucial for converting AI’s capabilities into business value. Think of them as the diplomats at the AI table, fluent in both Python and PowerPoint. A few years ago, this job might have been done by a savvy project manager or the data science lead who also doubled as a liaison. Now it’s recognized as a distinct skill set.
  • Head of AI (Emerging): When a company gets serious about AI, it appoints someone to lead the charge. The Head of AI (sometimes referred to as Chief AI Officer) is the leader. They direct AI initiatives and teams​, setting strategy, prioritizing projects, and often evangelizing AI within the organization. Compare this to traditional setups, where a VP of Engineering or CTO might oversee AI projects alongside everything else. Now, if AI is core to the business, it has its head honcho. This shows how strategic AI has become. It’s not just an R&D experiment; it’s a core business function that warrants executive oversight.
  • AI Product Manager (Emerging): Product managers have been around for a long time, but an AI Product Manager is a newer twist. This person manages the development and rollout of products powered by AI. They have to understand AI capabilities and limitations, user needs, and business goals simultaneously. According to Gartner, they manage the AI product development lifecycle​. In practice, that means they decide which AI features to build next, how to improve a machine learning-based product, and how to measure success (beyond just “accuracy” of a model). A traditional product manager might not have the technical depth for AI, so companies are bringing in AI-specific product managers to ensure that brilliant algorithms translate into products people love.
  • AI Architect (Emerging): Just as a software architect designs the high-level structure of an application, an AI Architect designs the blueprint for AI systems. They figure out how all the pieces – data pipelines, models, deployment infrastructure, monitoring, etc. – fit together in a scalable and secure way. This role designs a scalable AI system architecture​. Previously, architecture might have been left to a principal engineer or the chief technology officer. Still, now that the tech is specialized enough, having an architect focused on AI ensures the whole pipeline runs like a well-oiled machine. They choose the right tools, frameworks, and cloud resources, so the data scientists and developers can focus on their parts.
  • AI Risk and Governance Specialist (Emerging): As AI moves from the lab to mission-critical operations, the risks and rules around it get serious. The AI Risk and Governance Specialist ensures that AI systems follow regulations, ethical standards, and internal policies. They ensure AI compliance and manage risk, from checking for bias in models to providing an audit trail for decision-making, and coordinating with legal on data privacy. In the past, these concerns were often an afterthought or handled by a mix of IT risk managers and lawyers. But with AI facing scrutiny (think AI in hiring decisions or loan approvals), this dedicated role has emerged to keep AI on the right track proactively.
  • AI Ethicist (Emerging): Closely related, the AI Ethicist focuses on the ethical implications of AI. Just because we can do something with AI doesn’t mean we should. This role involves setting guidelines for responsible AI use, reviewing projects for potential ethical issues (such as bias, fairness, and impact on jobs or society), and educating the team on ethical AI practices. Gartner notes that an AI Ethicist addresses the moral implications of AI​. A few years ago, you might only have heard about these questions in academic circles or not at all. Now, companies are hiring ethicists to internally police their AI efforts so they don’t end up in the news for the wrong reasons.
  • Decision Engineer (Emerging): This one is fascinating. A Decision Engineer designs systems that help organizations make decisions, often weaving in AI models. It’s not just about predicting something with AI, but using those predictions to drive action. They create decision logic, possibly combining business rules with AI outputs, to automate or guide decisions. In essence, they design AI-driven decision systems​. Traditionally, a business analyst or software engineer might have coded decision rules into software. Now, with AI able to advise on decisions (like “which customers to target for this offer” or “what maintenance to do on a machine”), someone needs to engineer how those recommendations play out in fundamental business processes. That’s the Decision Engineer’s mission: ensure that AI recommendations are improving outcomes and align with business strategy.
  • AI Expert (Emerging): Some organizations designate an AI Expert – a seasoned professional with broad and deep AI knowledge who can advise on challenging problems and connect the dots between all these specialized roles. This isn’t always a formal title on a business card, but it reflects a crucial function: a go-to expert who can help the team navigate complex AI challenges and strategies. In traditional teams, this might have been the lead data scientist or even an external consultant. Now it’s increasingly an official role to ensure there’s always someone who “speaks AI” at an expert level in the room.
  • Business Expert & Business Owner (Roles in the Mix): Not every role in the new AI landscape is technical. Business Experts (subject matter experts in finance, healthcare, marketing, or whatever domain you’re applying AI to) and Business Owners (the people accountable for the business outcome of a project or product) are now firmly part of AI initiatives. In the past, they might have been peripherally involved – giving requirements at the start and checking results at the end. Now that AI projects are core to strategy, these business-side roles are at the heart of it. The Business Expert ensures the AI team doesn’t get lost in tech and forget the real-world context. The Business Owner makes calls on scope and resources, and champions the project in the boardroom. Essentially, AI teams have learned that without continuous business input, you can end up with technically superb solutions that solve the wrong problem. These folks have a seat at the table from the start.
  • UX Designer (Established, now vital for AI): Last but certainly not least, UX Designers. User experience has always mattered, but with AI, there’s a twist. AI systems can be unpredictable or complex, and a UX designer’s job is to make the interaction seamless and trustworthy. Whether it’s designing a chat interface for an AI assistant, explaining a model’s prediction, or the workflow around an AI feature, UX folks ensure that humans can use what the techies build. In traditional software, designers worry about where buttons go. In AI products, they also have to think about things like, “How do we show the user that the AI might be wrong?” or “How do we keep the user in control?” AI didn’t create this role – it existed – but AI has elevated its importance. Gartner even calls out “D&A and AI UX Designer” as someone who designs user experiences for AI apps​. The landscape is changing such that no AI project is complete without considering the human at its end.

The New Landscape: Collaboration is Key

The Collaborative AI Ecosystem

The most significant change in this new landscape is how these roles collaborate. Building an AI solution used to feel like a research project; now it’s more like constructing a space shuttle with a giant team. Each specialist covers their part of the mission. The AI Architect and Head of AI map the mission plan. Data Engineers and Analytics Engineers fuel the rocket with clean data. Data Scientists and AI Developers build the engine (the models), while prompt engineers fine-tune the dials. Model Managers and Model Validators double-check everything before launch. Model/MLOps engineers ensure the engine runs smoothly in orbit. UX Designers craft the cockpit so that pilots (users) can operate it. Meanwhile, AI Ethicists and AI Risk Specialists are in mission control, ensuring we don’t break any universal laws (or corporate ones). And the Business Owners/Experts, along with AI Translators, are the ones who said “We need a shuttle” in the first place and keep everyone aligned on the destination. 

This may sound like overkill – do we need that many cooks in the kitchen? But as AI projects move from prototype to production, from nice-to-have to must-have, companies are realizing that a single “AI guy” can’t wear all these hats effectively. Would you trust one engineer to design, build, test, deploy, and monitor a complex rocket? Probably not. Similarly, in AI, specialization means better outcomes. It’s a response to AI’s coming of age: we’re not just experimenting anymore, we’re operationalizing AI across entire organizations. 

This shift is exciting. It means AI is creating jobs, not just threatening them. A recent LinkedIn report showed that AI-related roles are among the fastest-growing jobs in the world. In one 2025 ranking, an AI Engineer was #1, and there were 16,591 new AI job postings in 2024 – a 59% increase from January to November​. And these aren’t just technical roles – they also span creative, strategic, and ethical domains. As an AI practitioner myself, I find it energizing—and a little dizzying—to see job titles like “Prompt Engineer” or “Decision Engineer” on the organizational chart. It means the industry is learning what it takes to make AI work in the real world. 

So, what’s next? This new roster of AI roles will undoubtedly evolve. Some titles might vanish (will we still need dedicated prompt whisperers in five years, or will AI models learn to understand us better?). Even AI leaders disagree: one predicts prompt engineers will be everywhere, while another predicts the role will vanish​. Other roles will emerge that we haven’t even dreamed up yet – perhaps “AI Auditor” or “AI Interaction Coach,” who knows. The key takeaway is that AI isn’t a one-person show anymore. It’s a team sport, and a diverse one at that. 

For businesses and tech professionals, the message is clear: the AI revolution is opening up opportunities for every talent, not just coders. Whether you’re a storyteller who can be an AI Translator, a principled thinker who can be an AI Ethicist, or a hardcore programmer aiming to be an AI Architect, there’s a place for you in this new AI era. The roles in AI are multiplying, and with them, the chances to innovate and make an impact are growing too. It’s an all-hands-on-deck moment, and frankly, that’s what makes this wave of AI feel less like hype and more like an enduring change in how we build technology. 

That’s why this explosion of new AI roles matters now: it shows AI is maturing. Just like the rise of web development created front-end, back-end, and full-stack roles, AI’s growth is spawning its ecosystem of specialists. The landscape is changing fast, but one thing’s for sure – it’s an exciting time to be in tech if you’re willing to adapt. I’m here for it, coffee in hand (hopefully not spilling it next time I scroll job posts).

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