AI Engineer Job Description

What "AI Engineer" means

When companies post "AI Engineer," they mean an engineer who builds and maintains software products powered by large language models — not someone who trains models from scratch. The role sits at the intersection of software engineering and applied AI: you're expected to integrate LLM APIs into production systems, build retrieval pipelines over company data, maintain the quality of AI outputs over time, and debug the probabilistic failures that traditional engineers have never had to deal with.

Across 585 active listings, the pattern is consistent: companies want engineers who can ship AI features end-to-end — from prompt design through deployment — and who can keep those features working reliably as models, data, and usage patterns change.

Required skills

Frequency = % of 585 active listings that mention this skill. Priority is derived from frequency and listing emphasis.

Day-to-day responsibilities

• 1Build and maintain LLM-powered product features — chatbots, assistants, document Q&A, structured extraction
• 2Design and implement RAG pipelines: document ingestion, chunking strategy, embedding, vector storage, retrieval and re-ranking
• 3Write and version-control prompts; run regression tests when prompts change; measure output quality
• 4Debug AI-specific failures: hallucination spikes, context window issues, retrieval returning irrelevant results, latency outliers
• 5Set up evaluation frameworks to continuously measure AI output quality in production
• 6Collaborate with product and design on AI feature development; translate requirements into working LLM integrations
• 7Monitor token costs and latency; implement caching and model routing to control spend at scale

How it differs from related roles