Four workers. Five paths. One outcome. They added AI to their job titles this year and landed roles that pay more, promise faster growth, and signal relevance in a market where every company claims to be all-in on artificial intelligence.
But the stories Business Insider collected reveal something deeper. The titles themselves have lost their anchor. What one firm calls an AI engineer another labels a forward-deployed engineer or an AI-augmented specialist. The result is confusion for candidates, headaches for hiring managers, and compensation bands that stretch from reasonable to ridiculous for work that looks remarkably similar.
Georgian Tutuianu made the switch at HubSpot. A software engineer with several transitions already under his belt, he kept a dedicated section on his résumé for personal projects. One involved building with AI. “It was a juicy project where I could talk about it, and that was good enough,” he told Business Insider. The interview never veered into algorithms. The project spoke for itself.
Jai Raj Choudhary took a more persistent route. He moved from a data-focused software engineering role into an engineer position at the AI agent startup StackAI. He reached out repeatedly to the cofounder on LinkedIn. He posted publicly about the product and offered advice. Persistence paid off. “It’s not like a 9-to-5 cushy job,” Choudhary said. The hours run long. Learning never stops.
Brit Morenus started at Microsoft more than a decade ago as an executive assistant. She later moved into gamification. When AI opportunities surfaced she earned certifications in game mechanics and spent three months focused on the technology. Her new title became senior AI gamification program manager. Her advice was blunt. “You need to learn how the technology works, not just use it.”
Sajani Lokuge came from UX design. She built a LinkedIn presence around design careers and AI that now counts roughly 26,000 followers. That public platform helped her step into an AI content manager role. “The technology is evolving so quickly that everyone is learning as they go, and AI skills can be learned,” she said. A portfolio of clear communication about technical topics proved decisive.
Then there is Natasha Crampton. She trained as an attorney and studied information systems alongside law. Today she serves as Microsoft’s first chief responsible AI officer. She sees real advantage at the overlap of fields. “A huge amount of the value” lies at the intersection of technical knowledge and insights from the social sciences, she explained.
The titles keep multiplying.
CTO Ivan Turkovic watched the explosion and called it the worst naming disaster the industry has seen since DevOps stopped being a practice and became a person on the org chart. In one week he saw postings for AI Engineer, Applied AI Engineer, Generative AI Engineer, GenAI Engineer, LLM Engineer, Prompt Engineer, Context Engineer, Agent Engineer, Agentic AI Engineer, RAG Engineer, AI Systems Engineer, AI Platform Engineer, AI Infrastructure Engineer, AI Reliability Engineer, Model Deployment Engineer, LLMOps Engineer, AI Ops Engineer, AIOps Engineer, AI Evaluator, Evals Engineer, AI Red Teamer, AI Alignment Engineer, AI Safety Engineer, Model Behavior Engineer, AI Trainer, Forward-Deployed Engineer, AI Solutions Architect, AI Product Manager, AI Strategist, Chief AI Officer, Head of AI, AI-Native Developer, AI-Augmented Engineer, and simply “Builder.”
Most of those titles, he wrote on his blog, collapse into three actual jobs. Some build product features using APIs. Others train or fine-tune models. The rest keep the AI plumbing from catching fire. “I have been a CTO for most of my adult life,” Turkovic explained. “I have hired, fired, onboarded, mentored, and occasionally been forced to explain to finance why a ‘Senior Applied Generative AI Engineer II’ and a ‘Staff LLM Engineer’ should sit in the same comp band.”
The chaos shows up in salary data too. LinkedIn’s 2026 Jobs on the Rise report ranked AI Engineer as the fastest-growing job title in the United States. A separate analysis found AI skills carry an 11 percent wage premium within the same firm and 5 percent within the same job title. Yet the proliferation of labels makes it hard for candidates to know what they are actually applying for and for companies to compare talent fairly.
At the same time entirely new categories have appeared. Business Insider reported in May on the surge in forward-deployed engineers. These specialists embed with customers to turn vague AI intent into working solutions. Postings for the role jumped 19 times in January 2026 compared with the prior year. Palantir CEO Alex Karp described them as “a seasoned waiter in a French restaurant” – equal parts service and deep product knowledge.
Other fresh titles include AI storyteller roles, often called evangelists at Anthropic and Adobe, AI philosophers or resident ethicists at labs such as Anthropic and Google DeepMind, and internal AI accelerators tasked with driving adoption inside large organizations. Gig platforms now hire people to provide feedback on model outputs, improv comedy for training data, or even everyday photos and videos. Some companies have begun calling certain generative-AI coders “vibecoders.”
And. The C-suite has joined the frenzy. Chief AI Officer positions have landed at PwC, Accenture, local governments, and beyond. Salaries for these roles can top $350,000 and reach seven figures in the largest organizations.
But the real signal may not live in the title at all. Recent discussions on X highlight growing skepticism. One poster noted that the most important AI job title of 2026 might turn out to be AI Compliance Officer as the enterprise gold rush gives way to audits. Others joke that the next big role will simply be the person who prevents teams from burning money on poorly implemented pilots.
So what actually works for professionals trying to make the shift? The workers who succeeded combined three habits. They built tangible artifacts – projects, public writing, portfolios – that demonstrated capability. They learned enough technical detail to speak credibly without pretending to be researchers. And they networked relentlessly, whether through repeated LinkedIn outreach or by growing an audience that signaled thought leadership.
Turkovic offers blunt counsel to both sides of the hiring table. Companies should standardize on a handful of titles such as ML Engineer or AI Engineer and put the specializations in the job description rather than the headline. Candidates should stop collecting titles like Pokémon cards. The skills and outcomes matter more than the exact string on the business card.
The workers profiled show that backgrounds in law, design, communications, administrative work, and traditional software engineering can all lead to AI roles. No single pedigree dominates. What matters is evidence of learning speed and the ability to translate between technical possibilities and business or human needs.
That translation skill may prove the most durable advantage. As model capabilities advance, the people who can frame problems clearly, verify outputs rigorously, and align systems with real-world constraints will command premiums regardless of what their title reads. The labels will keep changing. The underlying demand for judgment applied to powerful tools will not.
Companies pouring hundreds of millions into AI infrastructure want returns. They need humans who can deliver them. Titles are marketing. Delivery is everything.
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