The Two Types of AI Engineers Most People Confuse
The confusion comes from mixing two very different roles.
Some AI engineers work on the model itself:
- training
- fine-tuning
- optimization
- inference systems
But a large number of AI engineers today work on something different:
Turning foundation models into reliable products. That means building:
- RAG pipelines
- agent workflows
- evaluation frameworks
- guardrails
- AI observability
- data pipelines
- cost
- latency optimizations
- integrations with real business systems.
The hard part is making an AI system that is accurate, scalable, measurable, secure, and actually useful. The model is only one layer.
The engineering is everything around it.
Take a look at this reddit thread, link in the comments.
Overall, this post is largely accurate, but a few phrases could be made more precise to avoid overstating things.
Fact check
"Some AI engineers work on the model itself: training, fine-tuning, optimization, inference systems."
✅ Accurate.
These responsibilities are common for engineers focused on model development, ML infrastructure, or inference optimization.
"A large number of AI engineers today work on something different: Turning foundation models into reliable products."
✅ Mostly accurate.
Since the rise of foundation models, many AI engineers do focus on application engineering rather than training models from scratch.
"A large number" is appropriate because it doesn't claim a majority.
"That means building: RAG pipelines, agent workflows, evaluation frameworks, guardrails, AI observability, data pipelines, cost and latency optimizations, integrations with real business systems."
✅ Accurate.
These are all common responsibilities in modern LLM application engineering.
Not every AI engineer does all of these, but collectively they're representative.
"The hard part is making an AI system that is accurate, scalable, measurable, secure, and actually useful."
✅ Accurate.
Building production AI systems involves much more than choosing a model.
"The model is only one layer. The engineering is everything around it."
✅ Conceptually accurate, though it's a simplification.
In production AI systems, the model is one component among retrieval, orchestration, evaluation, monitoring, security, deployment, and integration layers.
Minor improvements
Instead of:
"The engineering is everything around it."
Consider:
"Much of the engineering happens around the model—retrieval, orchestration, evaluation, monitoring, deployment, and integration."
This is a bit more precise.
Overall verdict
Accuracy: 9.5/10 ✅
The post correctly distinguishes between:
Model engineering (training, fine-tuning, inference optimization)
AI application/platform engineering (building reliable AI products using foundation models)
The only caveat is that AI engineering is a broad field, and responsibilities vary across companies and teams. The post reflects current industry practice well without making major factual errors.