14.07.2026 aktualisiert

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Senior Data Scientist

Muenchen, Deutschland
Deutschland
PhD Computer Science
Muenchen, Deutschland
Deutschland
PhD Computer Science

Profilanlagen

klimenta_cv.pdf

Über mich

Senior Data Scientist & ML/GenAI Engineer mit Promotion in Informatik und über 10 Jahren Erfahrung in Machine Learning, LLMs, RAG, Recommender Systems und produktiven KI-Systemen.

Skills

JavaAgile MethodologieKünstliche IntelligenzAlgorithmusAmazon Web ServicesKünstliche Neurale NetzwerkeMicrosoft AzureGoogle BigQueryRundfunkC++Cloud ComputingComputerprogrammierungContinuous IntegrationGitHubSkalierbarkeitPythonPostgreSQLMATLABMachine LearningNeo4jScrumRecommendersystemeRedisTensorFlowData SciencePyTorchLarge Language ModelsSnowflakeKanbanScikit-learnXgboostBosnischApache KafkaSuchmaschinenDatabricks
Ich bin Senior Data Scientist / ML Engineer / GenAI Engineer mit Promotion in Informatik und langjähriger Erfahrung in der Entwicklung, Skalierung und dem produktiven Betrieb von Machine-Learning- und KI-Systemen. Mein Schwerpunkt liegt auf klassischem Machine Learning, Deep Learning, LLMs, Retrieval-Augmented Generation (RAG, inkl. Graph-RAG), Agentic AI, Recommender Systems und Knowledge Graphs.

Ich habe End-to-End-ML-Pipelines umgesetzt – von Datenaufbereitung (ETL/ELT, Spark, Hadoop), Feature Engineering, Modelltraining und -evaluation bis hin zu Deployment, Monitoring und Optimierung. Zu meinen Projekten zählen u. a. ein Recommender System im Produktivbetrieb (ARD Audiothek), Betrugs- und Anomalieerkennung, Pricing Engines, graphbasierte Vorhersagemodelle sowie LLM-gestützte Assistenzsysteme für Unternehmensanwendungen.

Technologisch arbeite ich primär mit Python (PyTorch, TensorFlow/Keras, scikit-learn, XGBoost/LightGBM), Graph Neural Networks (PyTorch Geometric), Vektordatenbanken (Milvus, Pinecone) und Streaming-Technologien (Kafka). Im GenAI-Umfeld habe ich praktische Erfahrung mit Prompt Engineering, RAG-Architekturen, LoRA/PEFT, LLM-Evaluation, Retrieval-Optimierung sowie Multi-Agent-Systemen (LangChain, LangGraph, LlamaIndex, dspy).

Ich verfüge über umfassende Cloud-Erfahrung:
AWS (SageMaker, Bedrock, Lambda, ECS, Redshift, Personalize),
GCP (BigQuery, Vertex AI, Recommender Systeme, Agent Development Kit),
sowie Azure Databricks für verteilte Datenverarbeitung. Containerisierung und Betrieb erfolgen u. a. mit Docker und Kubernetes.

Ich verbinde starke analytische Fähigkeiten mit Engineering-Mindset, arbeite sicher in agilen Teams und bringe komplexe KI-Systeme zuverlässig von der Idee in den Produktivbetrieb.

Sprachen

DeutschverhandlungssicherEnglischMuttersprache

Projekthistorie

Senior AI engineer

Wirtschaftsprüfung, Steuern und Recht

500-1000 Mitarbeiter

Objective: This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge. Used: Python, RAG, LangGraph, LangChain, deepeval, MCP

Senior AI Engineer - RAG, Agents

Objective: Creation of a multi-agentic system that helps a media house in their daily business. The first project was creating a RAG system via Google Filestore, ingesting data from more than 80 websites (belonging to this media house) - this system was then queried by the end uses, who would ask various questions about the new content (series, shows, movies, books). Another project was developed with Google Agent Development Kit (ADK), and was a multi-agentic system communicating with RAG, on top of which I build an analysis layer, reporting about the recent job posts suitable for the media house. I was responsible for the design and end-to-end development, including deployment at GCP. I also used MCP (Model Context Protocol) for this project.

Senior Data Scientist - NLP, Text Classification

Objective: classifying call trascription with a multi-class classifier. The labels were nested in a hierarchy.
Achievement: The solution involved an emsamble of binary classifiers, working with a class hieararchy (hierarchical classifier, OneVsRestClassifier). The preprocessing involved topic extraction (keywords) to enrich the features, as well as obtaining a number of embeddings (OpenAI API, QWEN, etc.)

Senior AI Engineer - RAG, GraphRAG, Agents

Objective: To create a multi-agentic system, supported by a Knowledge Graph, that automates the process of drafting a research paper. The system used multiple experts (OpenAI models) that ”collaborated” during the process of document drafting. The whole process was supported by a Knowledge Graph out of which we extracted useful information. Technologies used for this framework: LangChain, LangGraph, Smolagents, LlamaIndex, dspy. The project involved the use of Terraform and GitHub Actions (CI/CD Pipeline), via AWS. The initial application was deployed as a Streamlit app. I was responsible for the AI Engineering part, Knowledge Graph creation, and also deployment in AWS. This was a GenAI Engineer role,
also involving Prompt Evaluation and Retrieval Optimization (Langfuse).

Senior Data Scientist - Computer Vision, Object Detection

Objective: given a spectral image that depicts the value of the sensors’s readings,
classify the signals and identify novelties (anomalies).
Approach: Given that there was not sufficient labeled data, I had to rely on self-
supervised machine learning paradigms. To satisfy the customer’s request for fast
processing, I utilized one of the YOLO architectures. The system was developed
with the mmyolo framework.

Senior Data Scientist - RAG System Development

Objective: an LLM chatbot to help with HR-related inquiries.
Approach: I led the development of an advanced Retrieval-Augmented Generation
(RAG) system aimed at improving HR data retrieval processes. This system utilizes
the Milvus Vector Similarity Search Database and OpenAI’s API to efficiently source
and integrate extensive HR-related data, enabling it to respond to a broad spectrum
of HR inquiries.
Used: Langchain, Langraph, MCP, Agentic frameworks, dspy

Senior Data Scientist - Recommeder System

Developing a Recommender System powering one of the largest Audio on Demand platforms in Germany.

Senior Data Scientist - Anomaly Detection

Objective: Fraud detection.
Approach: I designed and developed a fraud detection model for a Middle-Eastern
Buy-Now Pay-Later platform. A critical step for this client involved data pre-
processing, during which I employed a graph-based approach to identify cliques of
fraudsters. This was the first successful machine learning project for this client.

Senior Data Scientist - Debt Collection

Objective: To predict the likelihood of loan repayment by bank customers, aiding
in their segmentation for tailored communication strategies (email, SMS, or phone
calls).
Achievement: Created a behavioral scoring machine learning model, now incorporated
into Receeve’s collection approach.

Senior Data Scientist - Pricing Engine

Objective: To predict vehicle transportation prices, providing dispatchers with a
reliable basis for pricing.
Achievements: Enhanced the machine learning model’s accuracy by approximately
40% through advanced feature engineering and implementing a two-tiered regression
strategy, combining residual and standard regression techniques powered by XGBoost.

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