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AI Engineer (m/w/d) - LLM Apps, RAG & Argentic Systems (german speaking)
<p>Standort: Remote (DACH-Zeitzone), Office Berlin optional<br>Anstellung: Vollzeit, unbefristet<br>Start: Ab sofort<br>Sprache: Deutsch C2, Englisch min. C1</p> <p>───────────</p> <p><strong>ÜBER SANDAN AI</strong></p> <p>sandan AI ist ein in Berlin ansässiges AI-Native-Startup für Enterprise-Kunden im DACH-Raum. Wir prägen, wie Marketing in einer agentischen Wirtschaft aussieht – und bauen die Plattform, auf der diese neue Generation von Marketing-Workflows läuft. Unser Anspruch: Software so zu entwickeln, wie Software 2026 entwickelt werden sollte – agentisch, iterativ, mit AI als integralem Bestandteil des EngineeringWorkflows.</p> <p>────────────</p> <p><strong>UNSERE ARBEITSWEISE</strong><br>Wir entwickeln agentisch. Claude Code und Codex sind Kern unseres Engineering-Workflows – nicht Spielerei. Wer bei uns einsteigt, arbeitet entweder schon produktiv mit diesen Tools oder will sich aktiv und tief einarbeiten.</p> <h2 id="aufgaben">Aufgaben</h2> <p>Du baust die Intelligenz, die in ArcGEN läuft. Du verantwortest unsere LLM-Pipelines, Agenten-Architekturen und Eval-Frameworks - vom Prototypen im Notebook bis zur produktiven Pipeline mit nachweisbarer Qualität.</p> <ul> <li>Design und Betrieb von RAG-Pipelines (Embeddings, Vector Stores, Re-Ranking)</li> <li>Architektur agentischer Systeme – Tool-Calling, MCP, Sub-Agent-Orchestrierung</li> <li>Aufbau und Pflege von LLM-Eval-Frameworks: Metriken, Benchmarks, A/B-Testing, Quality-Gates</li> <li>Prompt-Engineering und systematische Prompt-Optimierung</li> <li>Entwicklung ML-Modelle für Marketing-Use-Cases (Attribution, Targeting, Forecasting, Anomaly Detection)</li> <li>Überführung von Notebook-Explorationen in produktionsreife Pipelines</li> <li>Enge Zusammenarbeit mit unseren Full-Stack-Engineers an der Plattform-Integration</li> </ul> <h2 id="qualifikation">Qualifikation</h2> <p>Must-have:</p> <ul> <li>Praxis mit modernen LLM-Workflows in Production: RAG, Embeddings, Vector Databases (Pinecone, Weaviate, pgvector)</li> <li>Solide Python-Kenntnisse inkl. pandas, NumPy, scikit-learn sowie PyTorch oder TensorFlow</li> <li>Erfahrung im End-to-End-ML-Lifecycle: Datenaufbereitung, Training, Evaluation, Deployment, Monitoring</li> <li>Erfahrung mit systematischer Modell-Evaluation: Metriken, Benchmarks, Eval-Frameworks</li> <li>Deutsch C2 und Englisch C1</li> <li>Builder-Mentalität: Du lieferst Production-Systeme, nicht nur Notebooks</li> </ul> <p>Pluspunkte:</p> <ul> <li>Eigenentwicklung von Custom Agents, Sub-Agents oder spezialisierten Coding-Workflows</li> <li>Erfahrung mit MCP (Model Context Protocol) und Tool-Calling-Architekturen</li> <li>Praxis mit Agent-Frameworks (Mastra, LangGraph, Vercel AI SDK)</li> <li>Erfahrung mit Fine-Tuning, LoRA-Adaptern oder Distillation</li> <li>MLOps-Tools (MLflow, Weights & Biases, DVC)</li> <li>Background in Marketing-Analytics, Attribution-Modellen oder Performance-Marketing-Daten</li> <li>Kaggle, Open-Source-Beiträge oder Publikationen im ML-/AI-Bereich</li> </ul> <h2 id="benefits">Benefits</h2> <ul> <li>Wettbewerbsfähiges Gehalt mit jährlicher Anpassung</li> <li>Vollständig finanzierter Zugang zu Claude Max, Codex, GitHub Copilot, OpenAI- und Anthropic-APIs sowie allen relevanten LLM-Tools</li> <li>Compute-Budget für Experimente, Modell-Training und ML-Workloads</li> <li>MacBook Pro nach Wahl</li> <li>Remote-First, flexible Arbeitszeiten</li> <li>Quartalsweise Team-Meetups in Berlin oder Dubai (DIFC AI Campus, optional)</li> <li>Direkter Einfluss auf Produkt-Architektur in einer frühen Phase</li> <li>Steile Lernkurve in Agentic AI, LLM-Orchestrierung und AI-Native-Engineering</li> </ul> <p>Lebenslauf reicht. Wenn du GitHub, Portfolio, Kaggle oder ein<br>Open-Source-Projekt hast – sehr gerne mit dazu. </p> <p>Wir freuen uns auf deine Bewerbung!</p>
