Applied AI Scientist(m/f/d)

Nc Group·Berlinsenior

En corto

→Científico de IA aplicada que construye sistemas de IA autónomos y pipelines RAG para datos de redes telecom.
→Día a día: modelar, evaluar y operacionalizar soluciones ML y de IA en producción, con enfoque en calidad y observabilidad.
→Destacado: trabajas con 144 millones de datos diarios generados por crowdsourcing en redes móviles y fijas.

Fluent English

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¿Qué piden?

✓Doctorado en disciplina cuantitativa (Física, Matemáticas, CS, Ingeniería, etc.)
✓3 años de experiencia desarrollando sistemas ML o software en entorno industrial o de investigación
✓Experiencia práctica con agentes de IA y RAG
✓Habilidades sólidas en ingeniería de software en Python
✓Dominio en modelos ML (scikit-learn, XGBoost/LightGBM, deep learning)
✓Experiencia en producción de soluciones ML en la nube, preferiblemente AWS

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

Pythonscikit-learnXGBoostLightGBMdeep learning frameworksLLMRAGAWSMLOpsagentic AI systems

¿A quién escribirle en Nc Group?

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About us NET CHECK GmbH was founded in 1999 with the aim of improving the quality of communication networks. Since then, NET CHECK has developed into the leading partner of network operators and infrastructure providers of mobile and fixed networks of all technologies. Its core competencies include international network benchmarking (comparative measurements) as well as network planning and fault analysis. NET CHECK has one of Germany's largest crowdsourcing platforms, which generates over 144 million data points every day. Our commitment to quality and security has earned us the trust of scientific and government institutions. NET CHECK is headquartered in Berlin and is part of the NC GROUP, an owner-managed group of companies with a total of over 180 employees at five locations in Germany and one in Belgrade, Serbia. Job Summary: We build AI platforms that transform complex telecom network data—including drive tests, voice quality, broadband diagnostics, and signaling analysis—into clear, actionable insights. We are looking for an Applied AI Scientist who combines strong analytical thinking with practical engineering skills. You will model complex problems, develop and prototype ML and agentic AI solutions, and turn them into reliable, production-ready tools for real users while applying the latest developments in AI. Responsibilities Design and build agentic AI systems and RAG pipelines that can reason over technical documentation and live network data. Take ownership of testing and evaluation by developing evaluation datasets, metrics, and benchmarks for accuracy, retrieval quality, latency, and cost. Ensure system quality can be measured reliably and regressions are identified before reaching users. Develop and continuously improve our evaluation and observability practices, including offline and online evaluations, LLM-as-a-judge approaches where appropriate, regression testing, and quality dashboards for agentic AI and RAG systems. Turn data science insights into production-ready solutions, covering the full process from data analysis and modeling to well-tested, maintainable AI tools, including operationalization and MLOps. Develop and enhance machine learning models for classification, anomaly detection, root-cause analysis, and related use cases, and integrate them into the platform. Write clean, well-tested, and maintainable code, actively contributing to high engineering standards across the team. Research and experiment with new AI models, methods, and technologies, turning promising approaches into practical features and production solutions. Work closely with engineers, data specialists, and domain experts to develop effective solutions for real-world telecom challenges. Your profile PhD in a quantitative discipline such as Physics, Mathematics, Computer Science, Engineering, Computational Chemistry/Biology, or a related natural science, with strong hands-on experience in mathematical and computational modeling. Minimum 3 years of experience developing software or machine learning systems in an industry or research environment, alongside or following your studies. Hands-on experience with AI agents and RAG, including building and implementing LLM-powered systems in practice. Strong background in data science and machine learning, with experience using tools and frameworks such as scikit-learn, XGBoost/LightGBM, and deep learning frameworks. Strong software engineering and programming skills, particularly in Python, with a focus on clean, well-structured, tested, and maintainable code. Good understanding of the design and development of agentic AI systems, including orchestration, tools, memory, evaluation, and guardrails. A strong research mindset and enthusiasm for keeping up with developments in AI and translating new approaches into practical solutions. Fluent English, combined with strong communication, collaboration, and teamwork skills.

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