When headlines scream about AI replacing workers, they’re only telling half the story. Yes, AI is transforming the job market, but it’s also creating entirely new roles that didn’t exist even two years ago.
Companies like Walmart, KPMG, and Salesforce are now hiring for positions with titles like knowledge architect, orchestration engineer, conversation designer, and human AI collaboration leader, roles that were science fiction just a few years back.
According to the World Economic Forum’s Future of Jobs Report 2025, by 2030, work tasks will be nearly evenly divided: 47% performed primarily by humans, 22% handled mainly by technology, and 30% involving collaborative effort between humans and AI.
This isn’t about robots taking over. It’s about creating a new kind of workforce where humans and AI work together, each doing what they do best.
The Real Numbers Behind AI Job Creation
Before diving into specific roles, let’s look at the actual market data:
The PwC Global AI Jobs Barometer 2025 shows that AI-skilled workers see a 56% wage premium in 2024, up from 25% last year, demonstrating explosive demand for these emerging skills.
The median salary for AI jobs in April 2024 was $160,056, up from $144,986 in the previous year. These aren’t just niche tech positions, they are high value roles across industries.
AI could contribute up to $15.7 trillion to the global economy by 2030, primarily by amplifying human capabilities. That economic value doesn’t come from machines alone, it comes from people who know how to work alongside them.
16+ New AI-Driven Job Roles: What They Actually Do
1. Knowledge Architect
What they do: Structure and organize business data so AI systems can actually use it effectively. They define taxonomies, create data hierarchies, and ensure AI models have the right context to make intelligent decisions.
Where they work: Enterprise companies deploying large-scale AI systems
Skills needed: Information architecture, data modeling, understanding of AI data requirements, business analysis
Salary range: $82,000 – $160,000 annually
2. Orchestration Engineer
What they do: Design and build systems that coordinate multiple AI models and microservices working together. They create primitives to efficiently orchestrate model-serving microservices, managing dependencies and improving combined latency and robustness.
Real-world application: At Walmart, orchestration engineers work on AI conversational platforms, ensuring different AI services work seamlessly together to power shopping assistants and customer service channels.
Skills needed: System architecture, microservices design, API integration, understanding of AI model deployment
Salary range: $117,000 – $286,000 annually
3. Conversation Designer / Conversational AI Designer
What they do: Design natural, helpful conversations between humans and AI systems. They script conversations, test interactions, and optimize user experience for chatbots, voice agents, and virtual assistants.
Skills needed: UX writing, psychology, natural language processing understanding, empathy for user needs
Why it matters: Bad conversational AI frustrates users. Good conversation designers make AI interactions feel natural and actually helpful.
4. Human-AI Collaboration Lead
What they do: Study real-world workflows, design new patterns of human-AI collaboration, and generate insights that inform how AI models are built and deployed. They ensure humans and AI systems work together effectively rather than working at cross-purposes.
Real company example: OpenAI has hired Human-AI Collaboration Leads to observe how people actually use AI in their work, then translate those insights into better AI systems.
Skills needed: Workflow analysis, change management, understanding of both human psychology and AI capabilities, research methodology
Critical insight: This role isn’t purely technical, it requires understanding how real people work and what they need to succeed.
5. Prompt Engineer
What they do: Design and optimize instructions given to AI systems to produce effective, consistent outputs. They bridge language, context, and business needs.
Why it’s valuable: The difference between a mediocre AI output and a brilliant one often comes down to how you ask the question. Prompt engineers master this craft at scale.
Skills needed: Understanding of LLM behavior, clear communication, creative problem-solving, domain knowledge
Career path: Many prompt engineers transition from technical writing, teaching, or data analysis roles.
6. AI Ethics Officer / AI Ethicist
What they do: Develop guidelines and audit AI systems for fairness, bias, transparency, and responsible deployment. They ensure AI doesn’t perpetuate discrimination or cause harm.
Skills needed: Ethics philosophy, understanding of AI systems, policy development, statistical analysis for bias detection
Why it’s critical: AI in financial institutions could introduce risks such as discriminating against people with lower income, breaking privacy laws aimed at protecting customer’s personal financial data. Ethics officers help prevent these problems.
7. AI Solutions Architect
What they do: Design and implement AI systems that align with business needs, ensuring seamless integration, scalability, and efficiency. They work on crafting AI-driven solutions to solve complex business challenges.
Skills needed: Expertise in cloud platforms (AWS, Azure, GCP), strong knowledge of AI, machine learning, and deep learning algorithms, proficiency in system design and architecture
Salary range: $140,000+ annually in the U.S.
8. Forward-Deployed Engineer
What they do: Embed with client teams to tailor AI models to real business problems. They combine hands-on coding with customer-facing work to customize AI solutions on-site.
Skills needed: Full-stack development, client communication, problem-solving, adaptability
Unique aspect: Unlike traditional software engineers who work from headquarters, these roles require being comfortable working directly at client locations.
9. MLOps Engineer / ML Systems Architect
What they do: Manage the complete lifecycle of machine learning models, from deployment and monitoring to scaling and operations in production environments.
Skills needed: DevOps practices, containerization (Docker/Kubernetes), CI/CD pipelines, model monitoring, cloud infrastructure
Why it matters: Building an AI model is one thing. Keeping it running reliably at scale in production is entirely different.
10. AI Cybersecurity Specialist
What they do: Protect AI systems from emerging threats like model hacking, adversarial attacks, and AI-assisted cyberattacks. They secure data pipelines, model deployments, and AI infrastructure.
Emerging threats they handle: Prompt injection attacks, model poisoning, data extraction from LLMs, AI-powered phishing
Skills needed: Traditional cybersecurity knowledge plus understanding of AI vulnerabilities
11. Generative AI Engineer
What they do: Build and fine-tune models that generate content, text, images, video, audio. They adapt these models for specific business use cases.
Real applications: Creating marketing content at scale, personalizing customer communications, generating product images
Skills needed: Deep learning, transformer architectures, fine-tuning techniques, understanding of generative models
12. AI Trainer / Data Annotator / AI Labeler
What they do: Work at ground level to curate and label data used by AI systems. They correct model outputs and support training processes.
Entry-level opportunity: This role serves as an entry path into AI careers, requiring less technical background initially.
Skills needed: Attention to detail, domain knowledge for specialized labeling, consistency, understanding of labeling guidelines
Why it’s important: AI is only as good as the data it learns from. These professionals ensure that data is accurate and relevant.
13. Computer Vision Engineer
What they do: Design systems that interpret visual input from images and video, applying them in autonomous vehicles, manufacturing quality control, medical imaging, and surveillance.
Skills needed: Deep learning, image processing, PyTorch or TensorFlow, mathematics, domain-specific knowledge
Applications: From helping doctors detect diseases in medical scans to enabling self-driving cars to “see” the road.
14. AI Product Manager / AI Strategy Consultant
What they do: Define what AI products to build, how to integrate them into business strategy, and ensure alignment with company goals. They translate between technical possibilities and business needs.
Skills needed: Product management fundamentals, understanding of AI capabilities and limitations, strategic thinking, stakeholder management
Critical skill: Knowing when AI is the right solution, and when it’s not.
15. AI Governance & Risk Specialist
What they do: Monitor compliance, regulatory risk, ethical risk, safety, and oversight of AI deployments across organizations.
Why it’s growing: As governments introduce AI regulations, companies need professionals who understand both AI technology and legal compliance.
Skills needed: Risk management, regulatory knowledge, AI technical understanding, policy development
16. Agent Architect / Agent Orchestrator
What they do: Define how AI agents interact with each other and with human users. They design workflows that enable multiple AI systems to collaborate, where one agent fetches information, another analyzes it, and a third generates an outcome.
Emerging importance: As agentic AI systems become more common, coordinating multiple autonomous agents becomes crucial.
Skills needed: System design, orchestration frameworks, integration protocols, understanding of autonomous agent behavior
Additional Emerging Roles Worth Watching
AI Integration Specialist: Focuses on plugging AI tools into existing systems and workflows, emphasizing practical adoption and change management over new model building.
Model Validator / Model Auditor: Reviews AI models for accuracy, fairness, bias, performance degradation, and compliance with internal and external standards.
AI Policy Analyst: Studies how governments and organizations should regulate or respond to AI, crafts policy frameworks, and advises on societal implications.
AI Content Creator: Uses AI tools (text/image/video generators) to produce creative content while bridging traditional creative work with AI-augmented workflows.
Data Governance Manager (with AI Focus): Oversees data quality, access, lineage, and compliance in environments where AI uses data heavily, ensuring the data foundation is solid.
What Makes These Roles Different
They’re Hybrid by Nature
Most new AI roles require mixed capabilities. You need technical understanding AND business acumen. Or ethics knowledge AND engineering skills. Or creativity AND data literacy.
Unlike traditional AI roles that require deep programming knowledge, human-AI collaboration positions emphasize strategic thinking about where humans excel versus where AI excels, communication skills to translate between technical and business teams, and ethical reasoning to ensure responsible AI implementation.
They’re Constantly Evolving
76% of employees believe that AI will create entirely new skills that don’t yet exist. Job descriptions are still being written. Titles vary between companies. What works today might change tomorrow.
They Span the Experience Spectrum
These aren’t all senior roles. They range from entry-level positions like AI trainers to executive positions like Chief AI Officer. There’s a path in regardless of where you’re starting.
How to Actually Prepare for These Roles
For Technical Roles
Build AI/ML fundamentals: Understand how AI actually works. Take courses on machine learning basics, neural networks, and LLMs. Platforms like Coursera, edX, and fast.ai offer solid starting points.
Learn key programming languages: Python dominates AI development. SQL remains essential for data work. Add cloud platforms (AWS, Azure, GCP) to your toolkit.
Work on real projects: Theory only goes so far. Build small models, contribute to open-source AI projects, or create AI applications that solve real problems.
For Business/Strategy Roles
Understand AI workflows: You don’t need to code, but you should understand what’s possible and what’s not. Use AI tools yourself, ChatGPT, Claude, Midjourney, to gain practical experience.
Learn prompt engineering: Skills like prompt engineering, which involves crafting precise inputs to optimize AI outputs, are becoming indispensable. Practice getting better results from AI systems.
Combine domain expertise with AI knowledge: Your deep understanding of healthcare, finance, marketing, or education becomes exponentially more valuable when paired with AI literacy.
For Everyone
Develop cross-functional fluency: The most valuable professionals bridge different worlds. Understand both how AI works and how business works.
Stay adaptable: 76% of employees believe AI will create entirely new skills that don’t yet exist. Focus on transferable skills and maintaining a learning mindset rather than betting everything on one specific job title.
Build soft skills: Creativity, critical thinking, empathy, and communication become MORE important in an AI-augmented workplace, not less.
Get practical experience: Experiment with AI tools in your current role. Document what works and what doesn’t. Build a portfolio showing how you’ve used AI to create value.
What Job Seekers Should Know
Job Titles Are Still Fluid
The same role might be called “AI Strategy Consultant” at one company and “AI Product Manager” at another. Always look at what the role actually does, not just the title.
Geography Matters (But Less Than Before)
Tech-heavy companies in major cities led early adoption, but AI roles are now appearing across industries and locations. Healthcare, manufacturing, finance, and retail are catching up fast. Remote work has also opened opportunities.
Don’t Wait for Perfect Job Postings
Many of these roles are so new that companies are still figuring out what they need. Create your own path by combining your existing expertise with AI capabilities.
Use your network to identify companies experimenting with AI and express interest in helping them navigate human-AI collaboration challenges.
Entry Points Exist
You don’t need a Ph.D. to start. Roles like AI trainer, conversation tester, or content creator with AI skills can serve as stepping stones into the broader AI ecosystem.
What Employers Need to Consider
Invest in Reskilling Your Current Workforce
RBC is asking workers across functions to become familiar with using AI tools. Rather than only hiring new AI talent, leading companies are upskilling existing employees who already understand the business.
Create Clear Career Pathways
Simply adding AI to current processes often results in small gains. To see meaningful improvement, organizations should rethink how work is done. Define what career progression looks like in AI roles.
Foster a Culture of Learning
Make it clear that AI is there to help employees, not reduce staff. Involve workers early by asking how AI could improve their work. The companies successfully deploying AI create psychologically safe environments for experimentation.
Real-World Company Examples
Walmart: Hiring orchestration engineers and conversational AI specialists to power AI shopping assistants serving 80% of American households.
KPMG: Deploying AI integration specialists and developing their AI Incubator for Salesforce Agentforce to help clients implement autonomous AI systems.
Salesforce: Creating forward-deployed engineers who work directly with customers to build and optimize AI solutions using their Agentforce platform.
Morgan Stanley recently began testing chatbots powered by OpenAI’s GPT-4 with 300 advisers to help them easily pull up research and data, with plans to open it up to its 16,000 advisers.
OpenAI: Hiring Human-AI Collaboration Leads to study how people work with AI and translate those insights into better products.
These aren’t experimental pilot programs anymore, they’re production roles solving real business problems.
The Bottom Line: Preparation Over Panic
AI is creating jobs, not just destroying them. But these aren’t the same jobs we’ve had before. They require new combinations of skills, new ways of thinking about work, and new approaches to career development.
The winners in this transition won’t be those with the most technical knowledge or the deepest domain expertise alone. They’ll be people who can bridge worlds, combining technical understanding with business acumen, creativity with analytical thinking, and human insight with machine capability.
AI could contribute up to $15.7 trillion to the global economy by 2030, primarily by amplifying human capabilities. That value comes from the new roles, new workflows, and new partnerships between humans and AI that we’re only beginning to explore.
The question isn’t whether these jobs will exist. They already do. The question is whether you’ll be ready when opportunity knocks.
Quick Action Steps to Start Today
- Use AI tools yourself: Spend time with ChatGPT, Claude, or industry-specific AI tools. Understand their strengths and limitations firsthand.
- Follow AI developments in your industry: Subscribe to relevant newsletters, join AI-focused LinkedIn groups, attend webinars.
- Document your AI experiments: Start building a portfolio of projects where you’ve used AI effectively, even small wins count.
- Network with AI professionals: Reach out to people in these new roles and ask about their career paths.
- Consider certifications: Google, AWS, Microsoft, and others offer AI/ML certifications that demonstrate commitment.
- Focus on problems, not just tools: The best AI professionals solve business problems that happen to use AI, not the other way around.
The future of work is being written right now. These 16+ roles are just the beginning. Make sure you’re part of the story.
