175 geprüfte KI-Jobs in Integration & Automatisierung

Das wird geboten

Experience deploying and operating Machine Learning and AI solutions in cloud environments, preferably AWS; Experience with AWS Bedrock, ECS and S3; Experience with modern Agentic AI, RAG and LLM tooling such as LangGraph, Chroma, FAISS, LangSmith or Lang fuse; Experience with LLM evaluation, observability, prompt engineering and cost/latency optimization; Knowledge of MLOps and production ML practices; Experience with deep learning frameworks; Exposure to telecommunications, networking, signal processing or other data-rich technical domains. Key Technologies & Skills Python | Machine Learning | Data Science | Agentic AI | AI Agents | Generative AI | LLMs | RAG | Retrieval-Augmented Generation | LangGraph | Chroma | FAISS | LangSmith | Lang fuse | scikit-learn | XGBoost | LightGBM | MLOps | LLM Evaluation | AI Observability | Prompt Engineering | AWS | Amazon Bedrock | ECS | S3 | Anomaly Detection | Root-Cause Analysis Interested? If you are an experienced AI Data Scientist who enjoys combining scientific thinking with hands-on AI engineering and wants to build Agentic AI, RAG and Machine Learning solutions that solve complex real-world problems, we would be happy to hear from you.

Zur Position

For our customer in Berlin, Germany, we are urgently looking for an experienced AI Data Scientist with strong analytical and mathematical skills combined with a hands-on engineering mindset. In this role, you will model complex problems, develop and prototype Machine Learning, Agentic AI and Retrieval-Augmented Generation (RAG) solutions, and turn them into reliable, production-ready AI tools. You will work at the intersection of Data Science, Machine Learning and AI Engineering, developing intelligent solutions for complex, real-world technical and telecom challenges.

Candidates must already be based in Berlin or be willing to relocate to Berlin. Applicants must be eligible to work in the EU. Visa/work permit sponsorship is not provided.

This position is only available for employees. Candidates must be fluent in English. Tasks and Responsibilities Design, develop and improve Agentic AI systems and RAG pipelines that reason over technical documentation and live network data; Develop Machine Learning models for classification, anomaly detection, root-cause analysis and other data-driven use cases; Build robust AI evaluation and observability frameworks, including evaluation datasets, benchmarks, offline/online evaluations, regression testing, LLM-as-a-judge approaches and quality monitoring; Define and monitor key AI quality metrics, including accuracy, retrieval quality, latency and cost, ensuring regressions are detected before reaching users; Turn Data Science and Machine Learning concepts into production-ready AI solutions, covering data analysis, modelling, testing, deployment, operationalizing and MLOps; Write clean, well-tested and maintainable Python code and contribute to high software engineering standards across the team; Research and experiment with emerging AI, LLM and Machine Learning technologies, translating promising approaches into practical features and production solutions; Collaborate closely with engineers, data specialists and domain experts to solve complex telecom and network-related challenges.

AI Data Scientist Profile PhD in a quantitative discipline such as Physics, Mathematics, Computer Science, Engineering, Computational Chemistry, Computational Biology or a related natural science; Strong hands-on background in mathematical and computational modelling; 3+ years of experience developing software, Data Science or Machine Learning systems in an industry or research environment, alongside or following your studies; Hands-on experience building and implementing AI Agents, Agentic AI and RAG solutions; Strong background in Data Science and Machine Learning, including experience with tools such as scikit-learn, XGBoost or LightGBM and deep learning frameworks; Strong Python and software engineering skills, with an emphasis on clean, structured, tested and maintainable code; Good understanding of Agentic AI architecture, including orchestration, tools, memory, evaluation and guardrails; Experience evaluating and monitoring LLM and RAG systems, including retrieval quality, accuracy, latency and cost; Strong research mindset with an interest in emerging AI technologies and their practical application; Strong communication, collaboration and teamwork skills; Fluent English, both written and spoken.