AI role · Open offer

AI Data Engineer — Embeddings & feature stores

Market index

Focus: AI Insurance · Health AI SaaS

Paris · 500-1000 employees · Scale-up · GenAI: Partial

Full-time
Senior
Data platform
Hybrid
Munich, Germany · Hybrid
€78k–€98k

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

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At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: Munich, Germany · Hybrid
Compensation: €78k–€98k
Experience level: Senior
Team: Data platform
Benefits & perks

About this role

What the team is looking for and how this position fits.

Alan is hiring a Senior AI Data Engineer — Embeddings & feature stores on the Data platform team (Hybrid · Munich, Germany). This is a hands-on role in ML platform & MLOps—not a generic “AI enthusiast” post.

You will own training, deployment, observability, and cost control end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Munich data platform team powers embeddings refresh, ACL propagation, and GDPR-aligned deletes for regulated German enterprises. Pipelines must be explainable to auditors.

Hybrid with partial remote; German fluency preferred for stakeholder trust.

What you will do

Implement streaming + batch hybrid pipelines with exactly-once semantics where required.
Collaborate on schema contracts with security for row-level security emulation in vectors.
Improve observability for embedding drift and toxic content clusters.
Optimize Spark/dbt costs while meeting nightly freshness windows.
Document data lineage for DPIA updates.

What we look for

5+ years data engineering plus exposure to ML-adjacent workloads.
Expert SQL/Python; AWS/GCP hands-on; Kafka/Pulsar familiarity.
Meticulous documentation habits.

Nice to have

Lakehouse table formats (Iceberg/Delta).
Experience with EU cross-border data residency cases.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
AWS
Cloud primitives for training, storage, IAM, and cost-aware ML platform & MLOps deployments.
SQL

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

dbtTool
Regular use of dbt in the ML platform & MLOps stack—configuration, debugging, and team conventions.
SparkTool
Distributed feature generation and batch scoring on large tabular or event datasets.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Describe how you invalidated stale vectors after a schema change.
Several of these show up in recent shipped work—not only on your CV: Python, AWS, SQL, dbt, and Spark.
You have owned training, deployment, observability, and cost control in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Munich, Germany)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: €78k–€98k, benefits in At a glance) and your expectations align.

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What the employer asked for

Describe how you invalidated stale vectors after a schema change.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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