AI Data Scientist – Agentic AI & RAG | Berlin

All European Careers; Berlin; Vor Ort; EUR 85.000 - 120.000; Vollzeit

Zusammenfassung

All European Careers in Berlin seeks an experienced AI Data Scientist to model complex problems and prototype Agentic AI and RAG solutions for telecom challenges. You will work at the intersection of Data Science, ML and AI Engineering to deliver production-ready tools. Candidates must be based in Berlin and fluent in English.

Visa sponsorship is not provided. You will collaborate with engineers and domain experts to solve real-world data challenges using Python, ML libraries and modern AI

Qualifikationen

• PhD in a quantitative discipline with strong mathematical background.
• 3+ years of experience developing software, Data Science or ML systems.
• Hands-on experience building AI Agents, Agentic AI and RAG solutions.
• Proficient in Python and software engineering practices.

Aufgaben

• Design, develop and improve Agentic AI systems and RAG pipelines that reason over technical data.
• Develop ML models for classification, anomaly detection, root-cause analysis, and data-driven use cases.
• Build AI evaluation and observability frameworks, including datasets, benchmarks, and quality monitoring.
• Define and monitor AI quality metrics: accuracy, latency, cost; detect regressions early.
• Turn DS/ML concepts into production-ready AI solutions, covering data analysis, modelling, testing and MLOps.
• Write clean, well-tested Python code and contribute to high software engineering standards.

Kenntnisse

Python

Machine Learning

Data Science

LLMs

RAG

Agentic AI

English Fluency

Ausbildung

PhD in a quantitative discipline

Tools

scikit-learn

XGBoost

LightGBM

LangGraph

Chroma

FAISS

LangSmith

Lang fuse

AWS Bedrock

ECS

S3

Jobbeschreibung

AI Data Scientist – Agentic AI & RAG | Berlin

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.
  

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 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