ggplot2 Experts in Germany

• 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

Technologies: AWS, Hetzner, Kubernetes, Helm, Docker, vLLM, Pydantic AI, MCP, Qwen, Llama 3, MLflow, MinIO, GitHub Actions, Python, FastAPI, OpenTelemetry, Honeycomb, PyTorch, Polars, pytest

• Used OpenCode and AI-assisted software engineering to design, implement, refactor, test, and document an end-to-end RAW/SDTM/ADaM pipeline in R for Dr. Falk Pharma (07/2026), including metadata-driven transformations, automated validation rules and QC, traceability, and reproducible clinical outputs.

• Designed and implemented a robust Python-based data processing framework that supported the transition from R to Python and significantly improved data science productivity by providing maintainable, standardized modules for frequently used workflows, following coding best practices and DevOps principles
• Evaluated, trained, and deployed machine learning models on cloud platforms and edge devices, enabling fully automated mid-infrared (MIR) data evaluation pipelines that eliminated manual analysis steps and significantly shortened the time from measurement to prediction for customers and internal stakeholders
• Analyzed and interpreted multivariate MIR spectral data from the company’s proprietary analyzer using R and Python, supporting reliable identification and quantitation of chemical compounds in solution

• Prototyped LLM/RAG document assistant; trained transcriptomics and proteomic data; used Git/Docker for reproducibility.
• Strengthened ML fundamentals applicable to omics (feature engineering, validation, leakage control).

• Development & deployment of an interactive dashboard that allows users to select datasets for visual analysis.

• The application supports filters, AI-based interpretations, and a chatbot for user interactions.

• shiny, openai, mirai, plotly, mapgl, duckdb, AWS, ShinyProxy, Docker Swarm

• Architected end-to-end credit risk pipelines by orchestrating Airflow ETLs and training LSTMs/Transformers to predict default and prepayment speeds on MBS portfolios.
• Developed Deep Learning NLP solutions using BERT and LayoutLM for document processing, leveraging Transfer Learning and custom PyTorch loss functions to automate underwriting.
• Optimized R&D lifecycles through Bayesian tuning, Batch Normalization, and MLflow tracking to ensure robust model performance throughout volatile mortgage market cycles.
• Productionized scalable MLOps infrastructure via Docker and INT8 Quantization, deploying low-latency FastAPI microservices on AWS SageMaker with automated CI/CD pipelines.
• Ensured regulatory compliance by integrating SHAP/LIME for explainability and establishing real-time Data Drift monitoring to meet strict FHFA and Fair Lending standards.

• Developed web scraping scripts to extract and parse over 5000 product data from the Zalando website.
• Performed ETL processes using Apache Spark in CDSW, loaded the data into the Hadoop ecosystem (HDFS), and managed data using Hive and Impala.
• Implemented machine learning algorithms, achieving 85–90% accuracy on multi-class product classification.
• Integrated Zalando's product and price data into the dashboard with Otto and Takko for interactive visuals.

• Training Machine Learning models for different purposes.
• Programming with languages such as R and Python, depending on the project requirements.
• Develop, test, and maintain R shiny applications for semiconductor wafer fav production.
• Documentation of the code, project requirements, design decisions, and development processes.
• Perform integration testing, and debug to ensure software functions correctly.
• Coding, debugging, and ensuring quality standards for smooth operations.
• Deployment of Git and GitHub to manage changes and collaborate efficiently with fellow developers.
• Deployment of Docker and Jenkins for applications containerization.
• Staying updated on the latest tools, technologies, and best practices in software development.
• Providing back-up in MES (Manufacturing execution system).
• Getting involved in project management like task priority and project time lines.
• Maintaining and optimizing deployed Apps.