Builds products on top of existing foundation models rather than training new ones. The fastest-growing entry point.
An AI / GenAI engineer builds products on top of existing foundation models (GPT, Claude, Gemini, Llama) rather than training new ones. This is the fastest-growing AI role and the most accessible entry point into the field. You do not need deep ML knowledge — you need strong software engineering skills combined with understanding of LLMs, RAG, agents and evals.
The role emerged because foundation models shifted who creates AI from researchers to builders. Instead of training models from scratch, you design systems that use models effectively: retrieval pipelines, agent workflows, prompt templates, guardrails and evaluation suites.
Build features using LLM APIs, implement RAG pipelines from established patterns, write evals. Learn the landscape fast — this field changes monthly.
Design AI features end-to-end. Make architecture decisions (RAG vs fine-tuning, model selection, cost trade-offs). Own the eval pipeline.
Define the AI strategy for a product. Design multi-agent systems, complex retrieval architectures, and production-grade eval infrastructure.
Set technical direction for AI across products. Evaluate new models and paradigms, influence build-vs-buy decisions, mentor the team.
Already working in another field? Here is how your background maps.
This is the most natural switch. You already build products — now learn LLM APIs, RAG and evals. Your product instincts and system design skills are exactly what this role needs.
Very similar to full-stack. Learn LLM APIs and RAG. Your experience with APIs, databases and production systems makes you a strong candidate immediately.
Learn Python or TypeScript for backend LLM work, then LLM APIs and RAG. Many AI features are full-stack — your UI skills combined with AI backend skills are a powerful combination.
