Senior GenAI Data Scientist (m/f/d)

autoscout24senior

In short

▸Desarrollar sistemas de IA generativa autónomos (agentic) con enfoque en producción.
▸Integrar conocimiento en modelos mediante RAG, fine-tuning y búsqueda vectorial.
▸Destacar por liderar prácticas de ingeniería de agente y evaluación robusta de modelos.

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What they ask for

✓Experiencia sólida en IA generativa y modelos de lenguaje grandes.
✓Dominio de frameworks como LangChain, AutoGen o Atomic Agents.
✓Habilidades en diseño de pipelines de conocimiento (RAG, fine-tuning, búsqueda vectorial).
✓Experiencia en evaluación y monitoreo de modelos (drift, hallucinaciones).
✓Capacidad para traducir necesidades de producto en soluciones escalables.
✓Conocimiento práctico en ingeniería de software y workflows de código automatizado.

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LangChainAutoGenAtomic AgentsRAGFine-tuningVector SearchModel EvaluationModel MonitoringCI/CDGit

Who should you write to at autoscout24?

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A career driven by you. Join AutoScout24 Group and experience life in the fast lane. We’re not just leading the market; we’re creating it. We’re constantly innovating, evolving, and transforming things for our customers. We’re driven, ambitious, and determined to do our best work together. For a career that doesn’t stand still, where you can put your ideas in the driving seat and change the way the world moves, join our international community. As Senior GenAI Data Scientist , you’ll join our Data Science organisation and drive the development of next-generation AI solutions across the company. You will take ownership of building agentic GenAI systems, integrating knowledge into models, and setting up strong evaluation and monitoring frameworks that ensure we deliver reliable, scalable, and high-value AI products. This position is permanent and located in Munich (Germany) or Toronto (Canada). What you’ll do: • Design and build agentic GenAI systems using modern frameworks such as LangChain, AutoGen or Atomic Agents, taking the lead on translating product needs into scalable AI solutions and delivering production-ready implementations. • Drive harness engineering practices across Data Science projects, creating agent-ready repositories, workflows, standards, and feedback loops that enable software engineering agents to generate, test, maintain, and review code reliably across AI, ML, and data products. • Develop and maintain knowledge integration pipelines, including RAG architectures, custom model fine-tuning, vector search, and other grounding methods to ensure our AI systems have the right context for accurate, relevant outputs. • Define evaluation strategies, build ground-truth datasets, and create automated test and monitoring setups to ensure stable long-term model performance and proactively detect issues such as drift or hallucination. <li data-leveltext="" data-font="Symbol" data-listid="1" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left"

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