How do I transition from data science to AI engineering?

Data scientists already know how to frame an evaluation, read a confusion matrix, and reason about distribution shift. The gap into AI engineering is largely software engineering: writing reliable services, designing APIs, instrumenting observability, and shipping cost-aware code. Treat the transition as a software-engineering ramp, not a math one.

Start with the modern serving stack. Build a small but real RAG pipeline using a hosted LLM API (Anthropic Claude or OpenAI GPT), a vector database (pgvector, Pinecone, or Weaviate), and an embedding model. Ship it as a service with health checks, structured logging, and a basic eval suite. The interview signal you want to produce is end-to-end ownership.

Add the evals layer. Build a deterministic test set and an LLM-as-judge eval that scores quality on each commit. Read the MT-Bench and HELM methodology papers. Most AI engineering interviews now probe how you would catch regressions on a probabilistic system; if you cannot describe an eval suite from memory, study until you can.

Layer in cost engineering. Track tokens-per-second, dollars per request, and cache hit rate. Most production AI projects fail on cost before they fail on quality. Read the Anthropic and OpenAI rate-limit pages, the vLLM PagedAttention paper, and one quantization paper (GPTQ or AWQ). Knowing how to cut inference cost by 40% is interview-defining.

Then specialize. RAG engineering, agent engineering, and AI infrastructure are the three most common AI engineering tracks in 2026 hiring. Pick one based on the kind of system you want to own, and build one substantial portfolio project against it.

The conversion path lands in roles like AI Engineer, Applied AI Engineer, AI Platform Engineer, or AI Infrastructure Engineer. Compensation often climbs on the move because AI engineering benchmarks against frontier-lab levels, not analytics levels.