Three job titles. They sound similar on a job board. They do very different jobs.
Get the first one wrong and you lose months. You end up with a model specialist rebuilding a data pipeline, or a pipeline specialist trying to ship an agent. Good people, wrong seat.
Here is how we explain the difference to founders and CTOs, and how to decide who goes first.
The short version
AI Engineer. Builds product on top of large language models and other foundation models. LLM applications, retrieval (RAG), agents, tool use and evaluation. Ships features customers touch.
ML Engineer. Trains, tunes, deploys and maintains models. Owns the training pipelines, the serving infrastructure and the monitoring that keeps models honest in production.
Data Engineer. Builds the pipelines, lakes and lakehouses that everything else runs on. Gets the right data to the right place, clean, on time and trusted.
AI Engineer: when you are building with models
Most startups building with AI today are not training their own models. They are building on top of models that already exist.
That is AI engineering. The work looks like product engineering with a new set of tools. Prompting and tool design. Retrieval over your own data. Agents that take actions. Evaluation, so you know whether a change made things better or worse.
What to look for:
- Strong backend fundamentals first. An AI feature is still software. It needs APIs, data models, testing and someone who cares about latency and cost.
- Evidence of shipping LLM features into production, not just demos.
- A real answer to "how did you measure whether it worked?" Evaluation separates people who have shipped from people who have experimented.
ML Engineer: when the model is the product
If your edge is a model you train or fine-tune yourself, you need ML engineering.
This is a different discipline. Training and fine-tuning. Feature pipelines. Experiment tracking. Model serving. Monitoring for drift. MLOps, and increasingly LLMOps.
What to look for:
- Models they have taken all the way to production and kept running.
- Comfort with the infrastructure, not just the notebook.
- Judgement about when not to train a model at all.
Data Engineer: when your data is not ready
AI is only as good as the data underneath it.
If your data lives in five places, nobody trusts the numbers, and every new feature starts with a week of wrangling, your next AI hire will spend most of their time doing data engineering badly.
A strong data engineer builds the foundations: pipelines, a lakehouse or warehouse, and the quality checks that make data dependable. Databricks, Snowflake, Spark and streaming are the usual tools.
What to look for:
- Pipelines they designed and own, not just maintained.
- Clear thinking on data quality and governance.
- The ability to talk to product and engineering, not just other data people.
So who goes first?
A simple way to decide:
- Building AI features on existing models, and your data is in decent shape? Hire an AI engineer with strong backend skills.
- Your competitive edge is a model you train? Hire an ML engineer.
- Your data is scattered or untrusted? Hire a data engineer first. Everything after gets easier.
Many teams need all three eventually. The order matters more than people think.
The mistake we see most
Writing one job description that asks for all three.
"AI/ML Engineer with strong data engineering experience" reads well in a hiring meeting. In the market it attracts people who are strong at one of the three and stretched on the other two, and it makes the right specialist scroll straight past.
A job description does not define a role. A conversation does. Start with the problem you need solved in the next six months, then hire for that.
How we help
FinEx is a specialist talent partner for Software, Data & AI. We hire AI engineers, data engineers and software engineers from individual contributor through to Head of AI, Chief Data Officer and CTO, with the United States as our primary market.
If you are not sure which hire comes first, that is exactly the conversation we have on a brief call.