Prompt Engineers earn over $350,000, Model Managers control AI lifecycles, and AI Ethicists evaluate moral questions. 67% of companies create new AI roles – a complete transformation of tech teams is underway.
Just a year ago, "AI Prompt Engineer" sounded like science fiction. Today it's one of the most sought-after positions in the tech sector. Companies compete for talent that didn't even exist until recently.
The Data Scientist was once celebrated as the "sexiest job of the 21st century." Today, Data Scientists are still important – but they no longer work alone. Think of a film set: there used to be only director and actors. Today you need CGI specialists, sound designers, and stunt coordinators.
The lead actor in AI projects. Analyzes data, builds models, delivers predictions – but needs a whole support team today.
The silent heroes who keep data findable and clean. Without them, Data Scientists spend half the day cleaning up.
Connects Data Science with software development – brings models into production systems. Fills the gap between lab and reality.
Builds the infrastructure around AI – the app, interface, infrastructure. Without them, the model doesn't reach users.
Salary: $250K-$350K
Formulates the best questions for models like ChatGPT. Sounds simple but is crucial – good prompting ensures reliable results.
Salary: $180K-$250K
Structures company knowledge for AI consumption. Creates knowledge graphs and ontologies.
Salary: $200K-$300K
Bridges business and technology. Defines AI product strategy and prioritizes features.
Salary: $150K-$220K
Evaluates moral implications of AI decisions. Ensures fairness and transparency.
Salary: $180K-$280K
Monitors AI systems for bias, errors, and compliance issues. Critical for regulated industries.
Salary: $120K-$180K
Teaches AI systems through feedback and examples. Improves model performance iteratively.
Manages large language model infrastructure. Optimizes performance, cost, and reliability.
Designs and maintains vector databases for AI applications. Critical for RAG systems.
Protects AI systems from adversarial attacks. Implements security best practices.
Creates artificial training data. Solves privacy and data scarcity problems.
Start with Data Scientists, ML Engineers, and AI Product Manager. Build foundation for AI initiatives.
Add Prompt Engineers, AI Ethicists, and specialized roles based on use cases.
Expand with LLM Ops, Security Engineers, and domain-specific specialists.