Hire data engineers who feed your AI clean fuel.
The people who build the data foundation under every AI system: ingestion, pipelines, and the clean, governed data that decides whether your retrieval and your fine-tuning actually work. The unglamorous layer that quietly caps the quality of everything above it.
At a glance
The pipelines that turn raw data into retrieval-ready, training-ready fuel.
Review the work Data Engineers for AI can own, the skills required, and the systems they can help you build.
- Who this is for
- Engineering leaders who need an engineer to work within their existing team.
- Topics
- Airflow
- dbt
- Spark
- Kafka
- Warehouses
- Lakes
What they own.
- Build batch and streaming data pipelines
- Prepare and clean data for training and retrieval
- Stand up data ingestion from many messy sources
- Design schemas, lakes, and warehouses that scale
- Build the data layer behind RAG and analytics
- Keep data governed, traceable, and compliant
What you can ship with them.
Tools they reach for in production.
- Pipelines
- AirflowdbtSparkKafka
- Storage
- WarehousesLakesVector DBspgvectorQdrant
- Cloud
- CloudflareAWSGCP
- Stack
- PythonSQLStreaming
Seniority: Engineers who have built the data backbone for real AI products.
Five stages.
The top 3% remain.
Every stage asks the same question: can they keep AI running once real customers are using it? Getting something started is the easy part, and it is not what we screen for.
400 applicants
3%of applicants reach
your shortlist
What removes them
We start with something they built
100% → 20%One real system, pressed hard. How much traffic did it take? What broke first? Who got the call when it did?
We break something and watch them fix it
20% → 9%A working system with a bug hidden inside it. Anyone can build a demo in a weekend. Fixing code you have never seen is the actual job.
How will you know it is working?
9% → 5%Before they write anything, they have to tell us how they would test it, and what they would do when it gets an answer wrong.
Make it fast without running up the bill
5% → 4%We give them a speed target and a budget, then ask them to explain the tradeoffs they made to hit both.
It is late and the AI got it wrong
4% → 3%What do you do first? How do you find out what happened, undo it, and explain it to the customer in plain words?
Often hired together.
RAG & Retrieval Engineers
Retrieval specialists who ground every answer in your data.
Generative AI Engineers
All-rounders who ship a whole AI feature end to end, or anchor a pod of the specialists below.
MLOps & AI Infra Engineers
The infra people who keep AI fast, efficient, and observable in production.
Find the people to accelerate your roadmap.
You don’t need more resumes. You need proven AI engineers embedded in your workflow and ready to build from day one. Tell us what’s missing and we’ll line up a shortlist.