For the past few years, it’s felt like work discussions eventually turn into a conversation about AI. People are asking the same questions everywhere: What’s changing? What kinds of new jobs is AI creating? And how do you actually get into this space?
What’s interesting is that we’ve seen this pattern before.
Every major technological shift creates new categories of work even as it changes existing ones, and AI is following that pattern.
A few years ago, roles like AI governance lead or prompt engineer had limited existence outside of niche tech circles. Today, companies across healthcare, finance, retail, manufacturing, logistics, and consulting are hiring for them. Chief AI officer is now a common role at many Fortune 500 companies to lead enterprise AI strategy.
One of the fastest-growing areas that has seen a huge increase in the availability of AI job opportunities is training and evaluating AI models. The global demand for human evaluators and trainers is growing by 25% to 35% annually.
What’s emerging is a whole ecosystem of AI-related careers. The roles being created fall into three broad groups: technical roles, semi-technical & governance roles, and business & leadership roles. Each has different entry points and requirements. Read on to learn more.
What types of new and in demand jobs has AI created?
An AI position, put plainly, is a job that exists because AI systems need to be built, deployed, governed, or integrated into existing work. The World Economic Forum's 2025 Future of Jobs Report estimates that AI and big data specialists will be among the fastest-growing roles through 2030, but the report also highlights growth in non-technical positions tied to AI governance and strategy. Below, we’ll highlight some of the technical, semi-technical or governance, and business-related roles that AI is creating.
Technical roles
Technical AI roles focus on building, deploying, scaling, and maintaining AI models. These roles typically require a foundation in engineering and are especially common at software companies, AI startups, enterprise technology teams, and organizations building AI-powered products.
Applied AI/ML Engineer
Applied AI/ML engineers build and deploy AI-powered systems that solve real product and business problems. The role combines expertise in software development, data science and engineering, and programming. Unlike research-focused roles, the emphasis is on integrating, scaling, evaluating and optimizing models in production.
People who do well here typically have solid engineering fundamentals and/or 2–3 years of practical experience building and deploying AI systems. A PhD may be an advantage at research-heavy organizations, but for most companies shipping AI products, a bachelor’s degree is typically sufficient.
Explore: Software Engineer, Applied AI @ Mercor
MLOps/AI Infrastructure Engineer
MLOps engineers manage deployment, performance monitoring, security and compliance, and cost optimization. Essentially, they ensure AI models are reliable and affordable.
The strongest candidates come from DevOps or platform engineering backgrounds and layer on machine learning-specific tooling.
Explore: Infrastructure engineer @ Mercor
Agent Systems Engineer/Orchestrator
Agent systems engineers design and deploy AI systems that are powered by large language models, known as “agents.” They create rules and goals so that AI agents can make decisions, use tools, and complete tasks without human supervision.
This role typically requires experience in programming and a strong understanding of large language models. Many agent systems engineers have worked in backend or distributed systems engineering and moved laterally.
Explore: Software Engineer, Agents @ Mercor
Semi-technical and governance roles
Semi-technical and governance roles involve evaluating model outputs, testing for failures, writing the documentation that regulators and auditors require, curating the knowledge that retrieval systems depend on, and providing expert human feedback to improve models. These roles don't typically require a computer science degree, but those with domain knowledge may find more opportunities for higher-paying work.
These roles are growing rapidly as organizations face increasing pressure to manage AI risk, bias, transparency, and regulatory requirements.
Model Evaluator
Model evaluators design and run the test suites that determine whether a model is behaving correctly before and after deployment. They write evaluation rubrics, build regression tests, and flag when model updates break previously working behavior.
This role may be suitable for those with experience in QA engineering, data analysis, or clinical validation. Python expertise is also helpful. This is one of the most accessible technical-adjacent entry points into AI work.
Explore: Research Engineer – Benchmarking, Evals & Failure Analysis @ Mercor
AI Auditor
AI auditors verify that models meet internal standards and external regulations. They review models to make sure they work correctly, produce accurate results, and don’t have biases. This involves checking where training data was sourced, looking for bias in outputs, and documenting model behavior for compliance purposes.
A background in risk management, compliance, internal auditing, or legal work may be helpful in this role.
AI Governance Lead
AI governance involves overseeing the policies, rules, and processes that ensure AI use is legal and ethical. It helps companies to mitigate risks and build trust with stakeholders. It's a program management role with a specialized domain.
The strongest candidates for this role come from compliance leadership, privacy programs (GDPR, CCPA), or enterprise risk management. It requires understanding the technology well enough to ask the right questions, but a technical background typically isn’t necessary.
Domain AI Trainer/RLHF Specialist
AI trainers provide structured feedback to improve AI models. It involves tasks such as evaluating responses, preparing data, and labeling datasets.
This is one of the fastest on-ramps into the AI industry for professionals with deep domain expertise and no machine learning background. Pay rates vary by domain and seniority and are typically compensated hourly, with specialized domains such as medicine, law, and finance commanding significantly higher rates than general-purpose tasks. Platforms like Mercor connect domain experts, including software engineers, doctors, lawyers, and finance professionals, with AI labs and enterprises that need this kind of human judgment at scale.
Explore: Legal Expert - Transactional / Corporate | Radiology Expert | Hedge Fund Expert | Generalist Expert | Sales Consultant | Physics PhD Experts @ Mercor
Knowledge/Context Engineer
When an AI system gives incorrect answers, the issue is often related to the information retrieval system rather than the underlying model itself.
Knowledge engineers curate, structure, organize, and maintain the source information that AI systems rely on to generate accurate responses. Their work may include:
- Organizing documentation
- Structuring internal knowledge bases
- Improving retrieval systems
- Managing AI context pipelines
- Optimizing content for retrieval-augmented generation (RAG)
The goal is to ensure that AI systems emulate human judgment and behavior. Those with a background in technical writing, library science, documentation engineering, or information architecture will find this role maps closely to skills they already have.
Business and leadership roles
Those in business and leadership roles decide where AI should be used, design the workflows around it, manage adoption, and create the product roadmap. These roles suit people who understand both the technology's capabilities and its limits and can communicate across technical and non-technical teams.
AI Product Manager
AI product managers oversee the roadmap and development of AI-powered products and features. They work closely with engineers, designers, researchers, executives, and business stakeholders to determine how AI capabilities should be integrated into products and workflows.
Unlike traditional software systems, AI products produce probabilistic outputs rather than deterministic ones. Because of this, AI product managers must understand:
- Model limitations
- Hallucination risks
- Evaluation metrics
- Human-in-the-loop workflows
- AI safety considerations
- User trust challenges
Traditional product management experience is often highly transferable into this role. Professionals transitioning into AI product management should focus on building AI literacy around large language models, prompting, retrieval systems, and AI evaluation.
Decision Engineer/AI Workflow Designer
Decision engineers design the workflows where humans and AI systems work together. They determine which decisions the model makes autonomously, where a human does a review, what happens when the model's confidence is low, and how to measure whether the AI-augmented workflow outperforms the human one.
This is a strong fit for management consultants, business analysts, and operations leaders. Consulting firms, large enterprise transformation teams, and companies with dedicated AI centers may hire for this role.
AI Enablement Lead
Enablement leads run internal AI adoption programs. They train employees, build internal playbooks, coordinate with governance teams to make sure rollouts meet policy requirements, and track whether AI tools are actually being used and producing results. It's a learning-and-development role combined with program management.
This career may suit professionals with a background in learning and development, HR technology, or internal consulting. It's a fairly accessible and durable role—every organization adopting AI needs someone managing the human side of that adoption.
AI-Augmented Domain Specialist
An AI-augmented domain specialist uses AI tools to improve efficiency within their field, whether that’s healthcare, law, finance, marketing, education, or HR. They may look for ways to use AI tools to solve specific problems, evaluate AI systems, or train colleagues to use them.
These specialists typically already have deep knowledge of their field. By applying that knowledge to AI systems, they help create and implement tools that support human work.
Examples include:
- Lawyers using AI for contract review
- Marketers using AI for campaign analysis
- Recruiters using AI sourcing tools
- Healthcare professionals using AI-assisted documentation
- Financial analysts using AI for research workflows
As you know, the AI space is still evolving. As adoption expands, even more new roles may emerge in the coming years.
Industries seeing the most new AI jobs
The concentration of AI roles is shifting. Big tech companies still employ the most builders, but there is a growing demand in specialized industries where AI needs to meet regulations or perform complex workflows.
Healthcare and Life Sciences
Within the healthcare industry, there’s a need for clinical AI validators who test models against medical standards, medical AI trainers, and healthcare AI compliance officers. The regulatory environment means every deployment needs documentation, auditing, and ongoing monitoring. Governance roles are especially important here. Emerging healthcare AI roles include:
- Clinical AI validators
- Medical AI trainers
- Healthcare AI auditors
- Healthcare governance specialists
Legal and Financial Services
Large language model adoption for contract analysis, compliance monitoring, and financial modeling, fraud detection etc. This is creating demand for roles like:
- Legal AI trainers
- AI compliance specialists
- AI auditors
- Governance leads
- Workflow designers
Growing regulatory scrutiny around AI governance, like the European AI Act and SEC AI disclosure requirements have also increased the need for ongoing roles.
Technology and Software
This remains the broadest hiring ground for AI-related jobs. MLOps, agent orchestration, AI product management, and AI-augmented software engineering are all growing here. Companies building AI-native products often create new roles rather than retraining existing staff.
Government and Public Sector
Procurement mandates for AI systems now frequently require auditing and bias testing, generating demand for AI auditors, governance specialists, and ethics roles with public policy backgrounds. This is a quieter growth area than some other sectors, but the work is steady and the demand curve is just starting.
Consulting
There’s a growing need for both technical and non-technical AI-based jobs in consulting. Strategy firms need people who can advise clients on AI deployment, while implementation firms need people who can build and test systems.
How to break into these high-demand AI roles
AI careers don’t always require starting over completely. In many cases, professionals can transition into AI-related work by building on skills they already have. Here are some steps that you can follow as you’re thinking about making a pivot into AI:
- Identify which category fits your background: Do you have engineering experience, analytical and domain expertise, or business and leadership knowledge? Roles are continuing to evolve and emerge. Focus on an area where your skills lie.
- Pick a role that matches your skills: Read job descriptions to determine if you’re a good fit. Rule out roles that don't match your experience.
- Learn the specific AI skills you're missing: Figure out what you need to learn. For example, an auditor moving into AI auditing needs to learn how models are evaluated. A product manager moving into AI product management needs to understand probabilistic outputs.
- Create a sample, portfolio, or resume that demonstrates your skills: Proof of your knowledge or skills beats a degree or certificate.
- Apply to full-time positions or look for hourly AI training roles: You don’t need to reinvent your career to move into AI. Many AI roles build on skills you already have. A QA engineer might transition into AI auditing, while a technical writer could move into knowledge engineering. You can also start with short-term AI training or evaluation projects. These roles let you use your existing expertise to review model outputs, test systems, and provide feedback while building experience and getting paid.
Get started with Mercor
The growing AI job market has a variety of entry points matched to different backgrounds. Pick the one that fits, and start closing the skill gap between where you are and where that role needs you to be.
If AI training work fits your background, Mercor matches domain experts with AI labs that need human feedback. You can apply to train AI on Mercor or hire AI trainers at scale for large-scale evaluation and feedback projects.