What an AI engineer does
AI engineering is a broad field covering several distinct roles that sit between data science (building models) and software engineering (building products). An AI engineer typically works on integrating AI and machine learning capabilities into production systems: designing and implementing model training pipelines, building inference infrastructure that serves model predictions at scale, creating retrieval augmented generation (RAG) systems that connect language models to proprietary data, implementing MLOps practices (versioning, monitoring, and retraining models in production), and in some cases fine-tuning foundation models for specific applications.
The role is distinct from a data scientist (who focuses on model development and experimentation) and from a software engineer (who builds applications). AI engineers typically sit at the intersection, bringing enough ML knowledge to work effectively with models and enough software engineering skill to deploy them reliably in production.
The skills AI engineers need in 2026
Foundation: Strong Python programming (NumPy, PyTorch or TensorFlow for model work; FastAPI or Flask for serving); understanding of machine learning fundamentals (gradient descent, loss functions, evaluation metrics, overfitting/underfitting); familiarity with transformer architecture and how large language models work at a conceptual level. LLM-specific skills: Working with the major AI APIs (OpenAI, Anthropic, Cohere); building RAG pipelines (embedding models, vector databases including Pinecone, Weaviate, Chroma; retrieval strategies); prompt engineering at the system level; evaluation of LLM outputs; fine-tuning workflows using LoRA/QLoRA for open-source models (Llama, Mistral). MLOps and infrastructure: Cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML); model versioning (MLflow, DVC); monitoring deployed models for drift and degradation; containerisation (Docker, Kubernetes). Software engineering fundamentals: REST APIs, asynchronous programming, Git, CI/CD pipelines, and testing practices.
How to break into AI engineering
Most AI engineers have come from one of two backgrounds: software engineering (who developed ML/data skills) or data science (who developed engineering skills). The transition from software engineering is typically faster because software engineering fundamentals (code quality, deployment, testing) are harder to acquire than the ML concepts. The transition from data science requires significant investment in software engineering practices.
For someone starting from scratch: a computer science degree or strong self-taught programming foundation, followed by the fast.ai practical deep learning course and building real projects (a RAG application on your own data, a fine-tuned model for a specific task), is a credible path. Contributing to open-source AI tools, writing technical content about what you build, and building a portfolio of deployed AI applications is more valuable for job hunting than certifications alone. Salaries for AI engineers in the UK range from £60,000-£80,000 at junior level to £120,000-£180,000+ at senior level in London.