26.10.2026 aktualisiert
verifiziert
Premiumkunde
100 % verfügbarSenior AI/ML Engineer & Data Scientist | Production Systems | ex-EY, ING, ALDI
Düsseldorf, Deutschland
Weltweit
Master of Science in Agricultural EconomicsÜber mich
Senior Engineer with 7+ years of experience across deep-tech, retail, banking, and consulting. Experienced in delivering end-to-end solutions, from technical design and implementation to deployment, monitoring, and production support.
Skills
AI AgentsGitHub ActionsMLflowMLOpsApache AirflowData AnalysisMachine LearningSQLData ScienceGitFastAPIPytestPySparkGit FlowKubernetes
I help businesses solve challenges by understanding their underlying business needs and developing solutions that balance business value, technical feasibility, and long-term maintainability.
My services include:
Data Science & MLOps:
- Exploratory data analysis, feature engineering, training, evaluating, and fine-tuning machine learning models
- Building and optimizing ML training and inference pipelines
- Managing the ML lifecycle, including model versioning, deployment, monitoring, and retraining
AI Engineering:
- Designing and developing LLM-powered applications
- Building and optimizing LLM serving infrastructure
- Developing agentic systems, tool integrations, and Model Context Protocol (MCP) servers
- Evaluating and optimizing LLM applications for quality, performance, and cost
Supporting Capabilities: Data Engineering, Backend & DevOps
- Building and optimizing batch and real-time data pipelines
- Developing REST APIs, microservices, and backend services
- Designing and implementing CI/CD pipelines
- Refactoring and optimizing codebases, establishing automated testing and code quality checks
Erfahrung
Berufserfahrung5 - 10 Jahre
Freelancer seit2026
Sprachen
DeutschgutEnglischverhandlungssicher
Projekthistorie
- Took time off to recharge and prepare for self-employment
- Completed the necessary administrative registrations for self-employment
- Led end-to-end delivery of ML/AI applications for the QuantistryLab simulation platform, from requirements definition and solution design to development, testing, deployment, and production support, coordinating efforts across back-end, DevOps, and ML teams
- Designed and implemented a compound search system with an open-weights LLM, the Pydantic AI agent framework, and the Model Context Protocol (MCP), exposing it through FastAPI endpoints
- Deployed and configured vLLM-based LLM serving on Kubernetes
- Built CI/CD pipelines and Helm charts to automate deployments across staging and production environments
- Instrumented the search system with OpenTelemetry spans and traces, integrating with the Honeycomb observability platform to monitor component-level latency and functional errors
- Developed a custom evaluation dataset and implemented evaluations for retrieval relevance, answer correctness, tool-call accuracy, and end-to-end task success
- Reduced infrared spectrum prediction time by 83% (from 60 to 10 minutes) by replacing a quantum-mechanical model with a pretrained graph neural network; fine-tuned the model using PyTorch, built and containerized the inference pipeline with Docker, and integrated it into the platform
- Implemented an MLflow model registry with MinIO-backed artifact storage
- Advised sales and science teams on ML/AI applications for prospective client projects, providing technical expertise to shape proposed solutions and prototypes
- Designed and implemented training and inference pipelines for customer segmentation, customer lifetime value, and liquidity forecasting models on Databricks; orchestrated with Azure Data Factory
- Developed custom data drift and model performance monitoring using statistical methods; introduced MLflow for experiment tracking, model versioning, and model registry management
- Improved codebase quality and reduced the runtime of PySpark-based data pipelines by up to 50% through code refactoring, pipeline modularization, removal of redundant logic, and automated quality checks via CI/CD
- Standardized development and release workflows by introducing GitFlow branching and a monthly release cycle