Engineering pods for production ML, MLOps, and LLM integration mandates. Senior Consultants drawn from a 10+ year applied-ML base, including vLLM/RAG production stacks.
// Sector-specific pain
Many engineers can train a model. Few have shipped vLLM, RAG, or vector-DB pipelines into production at SLA. Senior Consultants in the pod carry that production scar tissue.
DACH AI teams routinely under-staff MLOps. The pod absorbs eval-harness, drift-monitoring, and deployment scaffolding so the in-house team can focus on model work.
Mandanten require AWS Frankfurt or equivalent EU-resident processing for vector indices and inference. The Compliance Shield encodes this in every Werkvertrag.
// 10+ years · Applied & Enterprise AI/ML
// Technologies
// Prior employer archetypes
// Standard pod · AI/ML
Stuttgart-anchored. DACH Liaison. Sole instructional interface to the Mandant.
10+ yrs production ML. Architecture, eval design, model selection.
PyTorch / vLLM / RAG / MLOps execution. Tier-1 alumni. Five-stage vetting.
// AI/ML-specific posture
AI/ML mandates frequently process PII and proprietary corpus data. The Compliance Shield ensures: (1) AVV under Art. 28 DSGVO with explicit training-data and inference-data scope, (2) AWS Frankfurt processing region for vector indices and inference, (3) ISO 27001 alignment (implementation in progress, target Q3 2026).
For mandates touching the EU AI Act risk classifications, niogod™ supports model-card and risk-management documentation as deliverables under the Werk. The Mandant retains all rights to trained weights and evaluation artefacts as work-for-hire under the Mandanten-GmbH/AG/UG.
A 30-minute strategy call with the DACH Liaison and a sector specialist. Outcome: a tailored R&D Intelligence Report within 5 business days.
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