Lead Data Engineer (AI Systems)
Can you build the data infrastructure underneath the AI product?
We are working with one of Munich's rising AI stars, who are building agentic AI solutions to change the Pharmaceutical and Life Sciences industry forever!
They aren't building yet another AI chatbot, their platform helps teams make sense of fragmented, messy and business-critical data, turning it into usable intelligence through AI-native workflows, automation and intelligent data infrastructure.
The company is now hiring a Lead Data Engineer, AI Systems to own the data layer that powers the product.
If you have built serious data pipelines before, understand orchestration and data quality deeply, and want to move closer to applied AI, this could be a strong fit.
You will take responsibility for the design, build and scaling of data pipelines that support AI-driven products. Building robust ingestion, transformation and validation pipelines for complex enterprise data.
Designing data infrastructure that can support LLM-powered extraction, automation and analysis.
Working with fragmented, unstructured and semi-structured data from high-value business environments.
Creating systems for data quality, evaluation, reliability and traceability.
Helping define the technical direction of the data function as the company scales.
Supporting and later leading more junior engineers as the team grows.
The strongest fit will be someone with a serious data engineering background who is excited by AI, rather than someone who has only built lightweight LLM applications.
Strong Python engineering experience.
Proven experience building production-grade data pipelines.
Experience with modern data tooling such as dbt, SQL, Postgres, data warehouses or data lakes.
Strong understanding of data modelling, data quality and pipeline reliability.
Experience working with messy, high-volume or business-critical data.
Interest in AI systems, LLM workflows, RAG, extraction pipelines or intelligent automation.
Experience building data infrastructure for AI, ML or LLM products.
Experience in regulated, sensitive or high-value data environments such as fintech, healthcare, pharma, insurance or B2B SaaS.
Experience mentoring junior engineers or growing a data engineering function.
Previous startup or scale-up experience.
The company is office-first in Munich, with flexibility around exact working hours and hybrid working. They are not offering fully remote work, but they are open to people structuring their week around where they do their best work.