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, operationalization 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.
Nice to Have
- 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 Langfuse;
- 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 | Langfuse | 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.