“AI engineer” is everywhere now. But it’s not the same as the traditional “ML engineer” that we know of.

In the old world, engineers built models from scratch. Now, engineers build systems on top of powerful LLMs. You do not invent the engine; you design the whole car around a strong engine that already exists.

This shift changes daily work, tools, and what “good” looks like. It also changes how we think about speed, cost, and risk.

In this article: we’ll recap traditional ML, show what changed with AI engineering, explain why evaluation is central, and end with a short example.

What Traditional ML Engineers Did

Traditional ML focused on the model.

• Choose a model (logistic regression, trees, neural nets).
• Collect and label data. Build features.
• Train, tune, and deploy the model.

Most problems had a single right answer (spam vs not spam, churn vs no churn). Teams measured success with accuracy, precision/recall, AUC, and ROC curves.

Inference usually ran on small or medium models. Batch jobs were common. CPU or a modest GPU was fine. Quantization and pruning helped but were not make‑or‑break.

The rhythm was simple: build the model, ship the model, iterate on the model.

The model was the main deliverable; everything supported it.

The Shift: What AI Engineering Changed

With LLMs, you adapt instead of training from scratch. You start from a strong pretrained model and change behavior through:

• Instructions (prompts and system messages)
• Context (retrieval and augmentation)
• Tools (APIs, search, databases)
• Finetuning (only when needed)

It’s still ML, but the focus moves from “train a model” to “design a system”—prompts, retrieval, tools, and how they work together.

Two practical changes stand out:

• Latency and cost matter a lot. Text generation happens token by token. Long prompts and long answers are slower and more expensive. Context length, decoding settings, and GPU throughput affect user experience and margin.
• Evaluation is harder and more important. Many tasks are open‑ended, so there is not one right answer. You must evaluate the whole setup (prompt + retrieval + tools + model), not just the model.

Evaluation: The New Backbone

In classic ML, you often evaluated after training. In AI engineering, evaluation guides the work from day one.

In practice:

• Build small, stable offline eval sets that match real user intents.
• Define simple rubrics that say what “good” looks like.
• Track a few task‑specific metrics and tie them to production sampling.
• Log traces. Version prompts. Record retrieved chunks and tool calls.
• When anything changes—prompt, retrieval settings, tool order, decoding—rerun evals.

Think in an adapt → evaluate → iterate loop, not just train → validate → test.

Now let’s look at a simple example.

Mini Case: From FAQ Bot to Support Copilot

To visualize the difference, we’ll solve the same support problem two ways: the old model‑first way and the new system‑first way.

Imagine you want to help support agents answer tickets.

Old world (traditional ML)

• Train an intent classifier and an FAQ ranker.
• Use labeled examples, small models, and standard metrics.
• Works well for predictable questions with fixed answers.

New world (AI engineering)

• Start with a capable LLM and a clear system prompt.
• Connect tools: ticket search, policy retrieval, account lookups.
• Add RAG for product manuals and internal runbooks.
• Define a short response schema so answers are structured.
• Evaluate not just hit‑rate, but also correctness, citation quality, tone, and when to escalate—offline and with production spot checks.
• If prompts + RAG still miss tone/format or are too slow/expensive, finetune a smaller model on curated conversations. This compresses instructions and reduces context.

Success now means something broader than “did it classify correctly?” It means “did the agent resolve the ticket faster, safely, and consistently?”

Traditional ML Isn’t Dead

Although LLMs are remarkably powerful and shift much of the work toward system design, traditional ML still matters a lot. Many high‑leverage parts of LLM systems are best served by small, supervised models with clear metrics and tight SLAs.

Input guardrails, for example, can classify safety, PII, language, spam, or policy compliance before the LLM runs. Lightweight classifiers are fast, cheap, and calibratable—ideal for hard blocks, tiered risk scoring, or selective redaction—and they reduce token spend while improving safety without adding much latency.

Bottom Line

In short: the current AI engineering means shipping reliable systems on top of strong base models. Start simple (prompting), add retrieval for missing facts, and only finetune when you need tighter control, lower latency, or lower cost. Keep evaluation at the center so quality stays high as you iterate.

Compared to traditional ML engineering—which centers on training and shipping a model optimized for well‑defined metrics;

AI engineering centers on orchestrating an end‑to‑end system (prompts, retrieval, tools, and policies) and proving it with scenario‑based evals and trace logging.

Reach for traditional ML when the problem is narrow, stationary, and label‑rich;

Reach for AI engineering when it’s open‑ended, knowledge‑heavy, or interface‑driven.

The best products blend both: small models for guardrails, routing, and scoring; LLMs for reasoning and language.