Anomaly Detection Experts in Germany

• Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
• Implementation of automated end-to-end cloud processes
• Development of LLM and NLP models
• Creation of interactive reports
• Support for national and international large corporations as well as medium-sized companies in implementing ML projects

Conversational AI assistant for cloud infrastructure and security queries

• Designed FastAPI backend with multi-turn conversation handler, token budgeting, and context window management.
• Integrated Amazon Bedrock (Claude 3 Sonnet/Haiku); built RAG pipeline with MongoDB chat history and semantic search.
• Implemented Factory Pattern for modular LLM provider switching; reduced model onboarding effort by 60%.
• Reduced LLM inference cost by 35% through model tiering (Haiku vs Sonnet) and prompt/entity consolidation.

Tech: Python, FastAPI, Amazon Bedrock, MongoDB, Streamlit, Pydantic.

• Designed and developed AI-powered document processing solutions using Python, OCR, NLP, and Large Language Models (LLMs) to automate extraction, validation, and classification of financial documents, reducing processing time by 75%.
• Built intelligent multi-stage workflow automation pipelines integrating AI services, machine learning models, and enterprise systems to streamline financial operations and improve data quality.
• Developed reusable AI-driven transformation frameworks capable of processing structured and unstructured document formats (XML, CSV, JSON, TXT, DAT) and normalizing them into unified business schemas.
• Designed and developed Python-based REST APIs and backend services supporting enterprise finance applications and high-volume data processing workloads.
• Built scalable data synchronization pipelines between Oracle CFIN and SQL databases, incorporating machine learning models for cash-flow forecasting and AP/AR anomaly detection.
• Architected and deployed Apache Airflow workflows to orchestrate AI-powered data pipelines, automating end-to-end processing from document ingestion through financial system integration.
• Led the migration of critical enterprise integrations from MuleSoft to Python-based services, improving maintainability, performance, and operational flexibility while preserving complete data integrity.
• Managed the full API lifecycle including solution design, implementation, documentation, deployment, monitoring, and production support for mission-critical financial systems.
• Collaborated directly with finance stakeholders to identify business challenges, define solution requirements, and deliver measurable operational improvements through automation and AI-driven workflows.
• Worked closely with cross-functional engineering and business teams to rapidly iterate on features, improve processes, and drive successful adoption of AI-enabled solutions.
• Provided technical leadership through architecture reviews, technology decisions, code reviews, and engineering best practices across integration and automation initiatives.
• Mentored developers, established coding standards, and contributed to improving software quality, maintainability, and delivery effectiveness across projects.
• Provided production support during critical month-end and quarter-close financial processes, performing root-cause analysis and implementing rapid fixes to ensure system reliability and data accuracy.

• AI Integration (LLM & RAG): Design and build of an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data for the automated analysis of technical documents and error logs.
• Design & Implementation: Design of a robust RS-232/UART communication interface for an SBC-based embedded device to control medical shaker systems.
• Architecture & Protocol Design: Implementation of a highly maintainable software structure (OOP, SOLID) and definition of hardware-close, resilient communication protocols including multithreading and advanced error handling.
• Quality Assurance & DevOps: Test automation using xUnit, integration tests directly on the hardware target, and maintenance of technical documentation according to strict medical technology standards via Azure DevOps.

Label: C#, .NET, LLMs, RAG, n8n, RS-232, UART, Multithreading, async/await, xUnit, gRPC/protobuf, Blazor, MudBlazor, EF Core, Visual Studio 2026, Azure DevOps

• Architected and deployed end-to-end machine learning pipelines across classification and prediction datasets, ensuring robustness and reproducibility through MLOps best practices.
• Contributed directly to LLM model output accuracy improvement by designing and engineering specialised prompts grounded in end-to-end ML and SciML pipeline logic.
• Developed training data for large language models by formulating coding problems that models could not resolve and subsequently documenting the correct solutions.

• Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
• Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
• Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
• Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
• Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
• Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
• Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
• Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
• Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
• Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
• Integration of RESTHeart to create a REST API for MongoDB.
• Build an Angular frontend to simplify data queries and master data maintenance.
• Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
• Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).

Predapp is a Sovereign AI and Infrastructure company building AI systems that organisations can own, control, and deploy on their terms, with full data sovereignty. As CTO and investor since 2015, leading the development of the Sovereign AI Platform alongside an advisory practice spanning AI strategy for enterprises, fractional CTO engagements, and technical due diligence for VCs, PE, and family offices.

• Architected the Sovereign AI Platform from zero owning technical vision, infrastructure design, and engineering roadmap; currently deployed at a European hospital, an automotive client in Germany, and two US startups, with active commercial discussions with two leading European hosting providers
• Dubai Health Authority (DHA / Nabidh): Designed and trained AI symptom checker and triage system for national 'Doctor for Every Citizen' initiative under HH Sheikh Mohammed bin Rashid Al Maktoum
• Emirates Airlines: Designed and deployed AI agent for ground personnel accelerating training, improving issue handling, and reducing cost of liquid workforce
• Developed explainable AI triage system piloted at University Hospital Heidelberg and Famagusta Hospital (Cyprus); reduced patient wait times by up to 15% (validation ongoing)
• Built production scheduling engine for US industrial AI startup: RL + Monte Carlo tree search, reducing planning from hours to seconds
• Designed and led the development of semantic search engines using RAG + Knowledge Graphs; developed Agentic Text-to-SQL solution for citizen data scientists
• AI strategy advisory and readiness assessments for enterprise clients, including architecture reviews, maturity assessments, and AI roadmap development

• Developed and deployed microservices in Kotlin and Spring Boot, integrated AWS Secrets Manager to secure credentials and decreased network calls using Spring cache.
• Refactored Kafka consumer using Spring Kafka with semaphore-based backpressure to cap records and keep heap memory stable under spikes; switched to batch upserts to cut down on database invocations; added Testcontainers integration tests for Kafka and database to pave the way for future changes.
• Automated the financial reconciliation workflow in Spring Boot (Kotlin) using Spring Scheduler, transactional boundaries, JPA/Hibernate on MySQL, and Flyway migrations, saving the accounts team 16+ hours per week.
• Designed and dockerized payments end-to-end test framework in Robot (Python) with reusable keyword libraries and profiles; integrated with GitLab CI (JaCoCo XML and HTML reports) to accelerate releases and lift code coverage to 80%.
• Implemented end-to-end observability on Datadog by instrumenting services with Datadog APM, correlating metrics and logs, provisioning dashboards, and creating monitors with burn-rate alerts and anomalies to harden reliability and give stakeholders clear visibility.

• Delivered AI and data science solutions, including LLM-based chatbots and data pipelines, improving operational efficiency.
• Collaborated on product features, achieving measurable impact and maintaining strong client relationships.

• Bedrock technology operations role providing the foundations on which PUMA's global product teams build PUMA.com and associated systems
• Managed a €3.5 million CAPEX portfolio including ~20 externals and 16 platform and tooling vendors
• Started up and led five functional teams for platform engineering, cloud administration, edge technology, continuous performance testing, and L2 support / incident management
• Person of last resort for mysteries, intractable bugs, compliance crises, litigation support, and black-hole issues where ownership was unclear or contested
• Sole author and driver of a multi-million-euro, multi-year RFP for Global Operations Center serving the direct-to-consumer technology estate for eCommerce, order management, and brick-and-mortar retail tech support
• Conceived, designed, developed and productionized Order Viewer, a secure internal application providing observability into PUMA.com order data at sub-second latency; independently delivered architecture, implementation, GCP deployment, SSO integration and organizational compliance.

Development and Implementation

• Conceptualization and implementation of the various requirements.
• Experience and ability to collaborate with other delivery teams
• Lead a team of 15 people taking care of matters including monitoring operations, delegating tasks and managing budgets.
• Act as product owner and represent the product vision, strategy and priorities in the area of production operations.
• Work closely with the members of the area including developers, analysts and QA to ensure the successful delivery of logistics features and improvements.
• Lead the process design and lead the build team for the SAP implementation (EU countries) and post-merger integration in SAP PP, QM, MM, APO and SD modules.
• Implemented and optimized MRP Live (MD01N) for multiple plants, improving planning runtime
• Worked with business users to transition from classic MRP to MRP Live
• Implemented SAP QM module, including quality planning and inspection lot processing, resulting in a 20% improvement in product quality compliance.
• Developed and tested custom reports using SAP Query and ABAP for MES data analytics, improving visibility into yield rates and resource utilization.
• Translate production requirements into clear and actionable user stories, acceptance criteria and technical specifications.
• Skilled in discrete, and process/manufacturing (PP-PI) environments; deep understanding of master data, production & process orders, recipe management, batch management, capacity & scheduling, shop floor control, and integration with QM, MM, EWM, MES.
• Worked in Variant configuration (VC)
• Supported international rollouts and transformation projects for SAP PP, QM, PPDS, aATP, EWM, and Variant Configuration within a global strategy.
• Tested functionalities for various business scenarios include interface systems
• Involved in SIT and UAT and cutover activities

Technology: Project Management, SAP PP, QM, MM, APO, ABAP OO, HANA Modelling, FIORI, SAP UI5, AMDP, SAP SD, SAP FICO and S4 HANA Emedded analytics

• Designed and built a multi-tenant Python/Flask API platform with JWT + API-key authentication, scoped access control, and service-level orchestration as the backbone for AI applications.
• Built a multimodal RAG system with hybrid chunking, dense/sparse embeddings, hybrid retrieval, reranking, and vector search to deliver grounded, high-precision responses across enterprise data.
• Productionized AI workflows with Docker, CI/CD, Redis-backed async job tracking, webhook callbacks, external AI/media service integrations, and runtime health/reliability controls.

• Program governance support: assisted in maintaining planning and resource utilization, ensuring alignment with scope, quality, and timeline constraints
• Meeting and communication strategy: facilitated organization of steering committees, working groups, and all-hands meetings; prepared materials, created meeting minutes, and coordinated logistics
• Project reporting and coordination: collaborated with project managers on comprehensive project deliverable reports and ensured effective communication across all levels
• Deliverables oversight: monitored and controlled project deliverables to meet program requirements
• Change initiative coordination: supported assessment of change impacts on the integrated program management plan
• Project management certifications: trained in Prince2, IPMA, certified Scrum Master, and Product Owner
• Problem-solving: proactively identified and resolved issues efficiently
• Analytical and organizational skills: exhibited strong analytical abilities and exceptional organizational skills in structured and unstructured environments

• Built a Trend-Scouting AI system to automate technology intelligence in power electronics and semiconductors, combining Azure OpenAI with LangChain, Scrapy-based web crawling for structured, noise-free data acquisition, and automated PDF reporting for internal R&D use. Developed a FastAPI-based (Uvicorn) web application to validate LLM outputs, test prompt strategies, and enable interactive system evaluation.
• Developed a real-time STM32 binary telemetry debugger with a PyQt-based GUI, featuring header-based frame synchronization, anomaly detection, template-driven payload decoding, time-aligned buffering, and live signal visualization.
• Developed an AI-driven power inductor designer using surrogate regression models for accurate electromagnetic and thermal prediction. Integrated multi-objective NSGA-II optimization to generate efficient, manufacturable designs.