Founding Data Engineer / Data Scientist

Every company wants its own AI. Almost none of them have their data ready for it.

Ask any organization where its knowledge lives and the answer is: everywhere. SharePoint, databases, PDFs, ticketing systems, email archives, a data warehouse in the cloud and a server in the basement. That data is exactly what makes a company's AI valuable, and exactly what makes it hard to build.

That's what we're solving at R3AL.AI.

We help organizations build their own AI: open models, fine-tuned on their own data, optimized and running on infrastructure they control. No sensitive data sent to third parties, no vendor lock-in. We work with insurers, government and healthcare organizations, where data privacy isn't optional.

Our platform runs the full cycle: data → fine-tune → evaluate → deploy, and then again whenever data grows or a better model comes out. The first step of that cycle is the one everything else depends on. That's where you come in.

We're early. Very early.

This is not a \"join a big team and own a narrow box\" kind of role. You'll be building the data layer of the platform from the ground up, together with the founding team.

What you'll do

As our Founding Data Engineer, you'll own everything between a client's raw data and a model that's ready to learn from it.

You'll:

• build connectors that let clients plug our platform into their data sources easily, whatever the format, tool or location: local, on-prem or cloud


• design automated pipelines that ingest, clean, deduplicate and structure data, from databases to messy unstructured documents


• turn raw company data into ML-ready assets: fine-tuning datasets, evaluation sets and retrieval-ready knowledge


• build in data privacy from the start: PII detection, anonymization, access control and data lineage


• make pipelines repeatable and versioned, so every new data drop or model release can flow through the cycle automatically


• work closely with the founders on the product roadmap, technical direction and how we onboard clients

Who you are

You probably have:

• solid experience as a data engineer or data scientist building production pipelines


• strong Python and SQL, plus experience with data processing tools (Pandas, Polars, Spark or similar)


• experience connecting many types of sources: relational databases, data warehouses (Snowflake, BigQuery, Databricks), object storage, APIs and document stores


• familiarity with orchestration and transformation tools like Airflow, Dagster, Prefect or dbt


• comfort working with Docker and deploying on both cloud and on-prem environments


• bonus points if you've prepared data for LLM fine-tuning, RAG or evaluation, processed unstructured documents (PDF, Word, email) at scale, or worked in regulated sectors with GDPR-sensitive data

But beyond the technical side, we're looking for someone who:

• likes ownership and has an entrepreneurial mindset


• thinks like a builder, not just an executor


• enjoys messy problems and turning them into clean systems


• wants to be early, not comfortable


• cares about building something that could genuinely matter

Because this is the kind of role that gives you far more than just a title.

You'll get real ownership from day one. Not just over pipelines, but over how the product works, what clients experience when they connect their data, and how we build the company.

You'll work directly with the founders, see your work go live at real clients, and get exposure to far more than engineering alone: client onboarding, go-to-market, strategy and the full process of building a company from the ground up.

And yes, this role comes with a strong compensation package that reflects the level of ownership, ambition and impact expected from an early hire.

You won't be joining as employee number 47 to maintain one tiny corner of a mature data stack. You'll be one of the first people shaping what R3AL.AI becomes, technically, strategically and culturally.

If you want a predictable corporate data role, this is probably not it.

If you want to build the foundation that lets every company own its AI, we should talk.