Description

I help organizations build AI-powered products, data platforms, and scalable web applications from concept to production.

I am a Data Scientist, AI Engineer, and Full Stack Developer with experience delivering solutions for international organizations, startups, and private-sector clients. My expertise spans Artificial Intelligence, Machine Learning, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), cloud architecture, geospatial analytics, and modern web development.

Currently, I serve as a consultant on United Nations projects and as Co-Founder & CTO of TradeComply, an AI-powered trade compliance platform helping businesses navigate international regulations, tariffs, and customs requirements.

My services include:

• AI & Machine Learning Solutions

• LLM Applications and RAG Systems

• Data Science and Analytics

• Full Stack Web Development (React, Next.js, TypeScript)

• Firebase & Cloud Architecture

• Geospatial AI and Remote Sensing

• API Development and System Integration

• SaaS Product Development

I focus on building reliable, production-ready solutions that deliver measurable business value. Whether you need an AI-powered application, a modern web platform, data-driven insights, or cloud infrastructure, I can help transform ideas into scalable products.

Industry field of expertise

Languages

English
Native or bilingual
German
Basic
Hindi
Native or bilingual

Workplace preferences

Can work on-site

Regensburg (up to 50km)

United Nations, OICT
Data Scientist - Tech Lead (Contract)
April 2025 - Today (1 year and 2 months)
f Designed and deployed an end-to-end building footprint extraction pipeline using PyTorch Lightning with ResNet and HRNet backbones, from data preparation to production-ready inference.
f Designed multi-class segmentation models to classify formal, informal, and temporary structures, en abling data-driven municipal planning and policy decisions.
f Developed a full-stack React application with a dedicated backend to serve model inference and expose geospatial output to end-users.
f Developed modular geospatial data pipelines for raster processing, mask generation, and vector data integration, including water service point datasets.
f Established reproducible ML workflows using MLflow for experiment tracking and DVC for data ver sioning, improving team collaboration and traceability.
f Optimized training and inference pipelines for NVIDIA GPU-accelerated environments on Windows; used Azure for scalable cloud training and experiment orchestration.
f Collaborated with municipal authorities and domain experts to validate outputs and align models with real-world urban conditions.
f Provided technical documentation, deployment guides, and training materials to support the transfer of the system and the local capacity building.
Machine learning Data science DevOps Fullstack artificial intelligence
CYNEFY GmbH,
Lead Data Scientist/Data Engineer
October 2021 - July 2024 (2 years and 9 months)
93 Regensburg, Germany
f Delivered end-to-end ML solutions, from business analysis to deploying predictive models with concept drift monitoring for sustained performance.
f Led a team of five to design and deploy ML models on AWS and Azure, cutting manual effort by 40% for automation and automotive clients, including a global car manufacturer.
f Built an NLP-based compliance automation system, reducing review time by 60% and enabling proactive regulatory adherence.
f Developed and deployed a domain-specific LLM to extract regulatory data, boosting compliance accu racy by 35%.
f Created a real-time analytics pipeline for a global automotive client, automating reporting across 35+ countries via ETL, data lakes, and Power BI.
f Implemented MLOps pipelines with CI/CD, enabling automated retraining, monitoring, and deployment in cloud environments.
DevOps Machine learning Data science Natural Language Processing (NLP) Fullstack
NoseDat GmbH,
Data Scientist/Data Engineer
January 2021 - September 2021 (8 months)
93 Regensburg, Germany
f Developed a sales forecasting model for 25,000+ products, reducing stock-outs and overstock by 20%.
f Deployed and optimized ML models in production, ensuring scalability, automation, and seamless integration with existing business processes.
f Implemented end-to-end ML model deployment pipelines, incorporating automated monitoring, retrain ing, and performance tracking to maintain model accuracy and business impact.
f Optimized ML workflows on Microsoft Azure, leveraging cost-efficient compute resources (Azure ML, Data Factory, Databricks) to enhance model performance and scalability.
Data science Data analysis Microsoft Azure Machine learning Database Management (e.g., SQL, NoSQL)