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About the Role
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. Work at the intersection of Data Science, Machine Learning and AI Engineering, developing intelligent solutions for complex, real-world technical and telecom challenges.
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.
Requirements
- 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.
- Candidates must already be based in Berlin or be willing to relocate to Berlin.
- Applicants must be eligible to work in the EU.
- This position is only available for employees.
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
- 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
Location
- Berlin, Germany
Work Type
- Onsite
Experience Level
- 3+ years of experience
Education Level
- PhD
Equal Opportunity
- Visa/work permit sponsorship is not provided.