Question 1

What does "production serving experience" mean at shortlist time?

Accepted Answer

Concrete bars: (1) the candidate has owned a serving stack handling at least 10M model inference requests per month; (2) they have made at least one explicit cost/latency tradeoff and can show the numbers from before/after; (3) they have shipped at least one EU-region-only deployment with documented data-residency boundaries. Two of three is required for DACH shortlist; three of three is the senior+/staff bar.

Question 2

Are CEE ML platform engineers familiar with EU-region inference constraints?

Accepted Answer

Most senior CEE ML platform engineers have shipped EU-region-constrained workloads since 2022 — the constraint is well-known and well-engineered against in DACH and Western European delivery work. The 80th-percentile candidate has shipped multi-region routing with EU-residency keywords as first-class architecture, not as a post-hoc filter.

Question 3

How do you handle DSGVO Article 30 records-of-processing for ML platform work?

Accepted Answer

Every DACH ML-platform engagement we onboard documents the candidate's contribution to Article 30 records-of-processing, Article 32 security-of-processing, and where applicable Article 35 DPIAs. The candidate is briefed on the engagement-specific DSGVO posture before they ship; their commits are reviewable as part of the audit trail your DPO maintains.

Question 4

Can a senior ML platform engineer also cover MLOps for classical ML pipelines?

Accepted Answer

Yes — most senior CEE ML platform engineers in our pool have classical ML MLOps experience predating their LLM-serving work. Concretely: scikit-learn / xgboost pipeline orchestration via Airflow, model-registry hygiene via MLflow, feature-store design (Tecton, Feast). For DACH clients still running classical ML alongside LLMs, the candidate covers both surfaces.

Question 5

How do you handle Scheinselbstständigkeit for staff-track ML platform engineers?

Accepted Answer

Two paths. Pure B2B for senior ML platform engineers passes §7 SGB IV cleanly when structured correctly (registered foreign business, multiple clients, own tools, control over working pattern). For staff-track engagements where retention is the priority and the engineering pattern leans long-term-exclusive, we recommend EOR via Remote / Deel / Oyster — the engineer is formally employed in their home country, your German GmbH has zero direct employment exposure, and §7 SGB IV is out of scope.

Question 6

How does the 90-day replacement guarantee work for such a senior, infrastructure-heavy role?

Accepted Answer

Same shape as every other engagement: replacement search at no fee if the hire leaves or is let go within 90 days. Senior ML platform engineers stick because the role is engaging — high-judgement, infrastructure-heavy, owned end-to-end. DACH retention 96% across 2025-Q4–2026-Q1 (small sample, n=3, but no triggers).

Seniority	DACH-local	Recruo CEE-equivalent	Recruo fee (15%)	Personalberatung (25%)	Saving
Mid (3–5y)	€78–98K	€48–65K	€7.2–9.8K	€19.5–24.5K	€11–14K
Senior (5–8y)	€110–140K	€62–88K	€9.3–13.2K	€27.5–35K	€16–22K
Staff / Tech Lead (8y+)	€140–175K	€88–115K	€13.2–17.3K	€35–43.8K	€21–26K

Hire ML platform engineers for DACH scale-ups — MLOps, model serving, EU-region inference.

Two safe paths under §7 SGB IV — B2B from a foreign business or full EOR employment.

Human-in-the-loop is the default, not a retrofit.

A senior CEE ML platform engineer saves €25–35K vs DACH-local hiring, every year of the engagement.

What a placement actually costs vs Berlin/Munich-local hiring.

What a placement actually costs

Why ML platform engineering in DACH is a different shape than the same role in the US

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