AI Role Guides for the UK
16 Specialisms. Real Salary Data.

Extract meaningful insight from data and build predictive models. The discipline that bridges statistics, machine learning, and business decisions.

The most in-demand AI role in UK tech. Build, deploy, and maintain AI-powered systems from LLM applications to computer vision pipelines.

Build generative AI systems across text, images, video, and audio. Diffusion models, multimodal systems, RAG, fine-tuning, and real-time generation.

Build the application layer for language models — RAG pipelines, evaluation systems, prompt architectures, and LLM-powered product features.

Train and deploy ML models that work reliably at scale. The bridge between data science experimentation and production engineering.

Build the infrastructure that makes ML systems work reliably in production — training pipelines, model serving, monitoring, and automated retraining.

Build systems that see and understand the visual world. Object detection, image segmentation, video analysis, and edge deployment across automotive, medical, and retail.

Build systems that process, understand, and generate human language — from entity recognition and semantic search through to production LLM applications.

Ensure AI systems are reliable, aligned, and safe. Alignment, red-teaming, interpretability, robustness — one of the most important and talent-scarce roles in UK AI.

Build dialogue systems that sustain meaningful conversations. Chatbots, voice assistants, multi-turn agents, and LLM-powered conversational products.

Design, test, and iterate on the prompt systems that define how LLMs behave. A discipline that requires equal parts engineering rigour and writing clarity.

Advance the scientific frontier of AI. From industry research labs to academic institutions, researchers push the capabilities that power tomorrow's products.

Define what AI products should do, translate user needs into model requirements, and ship AI features that work reliably for real users.

Bridge AI vendors and enterprise clients. Technical demonstrations, proof-of-concept builds, RFP responses, and customer onboarding at hyperscalers and AI vendors.

The commercial-technical hybrid role. Deliver compelling demos, build POCs, and drive enterprise AI deals as the technical voice in the sales organisation.

Build autonomous agent systems that think, decide, and act — coordinating LLMs, tools, and data sources into reliable multi-step workflows. No CS degree required.