ClickHouse Experts in Germany

• TrendReel, production LLM agent and RAG system (Python, LangChain, OpenAI, Groq/Llama 3, Claude, FastAPI, Kubernetes, PostgreSQL).
• Designed and built a production multi-step LLM agent system: a script generation agent with a per-platform psychology database, 7 viral narrative frameworks, and structured quality scoring, switching between Claude and Groq backends in real time based on output metrics.
• Implemented multi-provider LLM routing (Claude primary, Groq/Llama 3 fallback) with priority-chain failover and quality-based provider switching, achieving 95% inference cost reduction while holding measurable quality thresholds.
• Built an advanced RAG-style retrieval pipeline with per-platform knowledge bases, semantic content matching, and structured output evaluation across 7 decision frameworks, directly analogous to multi-tenant context-based reasoning for enterprise document workflows.
• BrainyAI, adaptive AI learning platform (Python, LangChain, Groq Llama 3.3-70B, OpenAI, Next.js, Supabase, Redis).
• Integrated Groq Llama 3.3-70B with education-level-aware prompting, dynamically adjusting vocabulary depth, citation complexity, and reasoning style across four student proficiency tiers.
• Nexus Prime, multi-tenant SaaS platform for marketing and growth automation (25 modules, 99 backend routers, 153 frontend files).
• Built a 25-module, 99-router multi-tenant SaaS platform covering ad remix, affiliates, WhatsApp inbox, email, and cart recovery, serving four subscription tiers from $199 to $1,999 per month with integrated Stripe, Paystack, and Flutterwave billing.
• AI Video Surveillance Platform, multi-tenant edge and cloud computer vision system currently in active client pitch.
• Designed a multi-tenant AI video surveillance platform combining edge YOLO26 inference on NVIDIA Jetson Orin NX boxes with a central GKE cloud layer (Postgres, Pub/Sub, ClickHouse, R2, Keycloak) for event storage, dashboards, alerting, and multi-tenancy.

• 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.

• Contributed to the design of the new financial services integration layer and moderated the architectural discussions
• Oversaw the security concept and approval of the application
• Prepared infrastructure setup and best practices for the Kotlin backend

• New connection of the product interfaces of the Financial Data Warehouse (FDW) directly to the Abacus 360 native interfaces
• IT environment: PC, client-server, Citrix remote client, Oracle DB
• Mapping of FDW outbound to Abacus 360 Native
• Analysis of requirements documents, business concepts, and existing interface rules
• Definition of new FDW rules for outbound to Abacus 360
• Adjustment of test scripts for the new interfaces
• Execution of tests to ensure correct data delivery and processing

• Insurance service provider*

Challenge: Business-critical documents were processed through external OCR providers, with ongoing costs, dependency, and data privacy risks for sensitive insurance data.

Implementation:

• Architecture and production implementation of an on-premise OCR solution with full data ownership
• Methods for recognizing document structures as the basis for automated further processing
• ML-, NLP-, and LLM/VLM-based information extraction, especially from invoices and quotations

Success: Replaced external providers: full data ownership, GDPR-compliant processing, and 75% lower recurring OCR costs per year

Used technologies: Python, Docker, Microservices, FastAPI, PyTorch, Torchvision, MongoDB, MySQL

• Assessment of the existing reporting landscape and strategic bundling of needs
• Migration and consolidation of reports to Metabase, connected to ClickHouse as the data foundation
• Building and maintaining data pipelines

Stack: Metabase · ClickHouse · Appsmith · Airflow

• Designed and deployed scalable REST APIs for enterprise clients
• Led migration of legacy systems to modern maintainable system, reducing deployment time and enabling daily deployments
• Mentored junior developers and collaborated cross-functionally to align technical roadmaps with business goals, improving team velocity by 50%.
• Tech Stack: PHP (Symfony), Golang, PostgreSQL, MySQL, GCP, Terraform, Kubernetes

• Designed and developed the "Credit Risk Engine" using Camunda, Node.js and Typescript
• Greenfield project for credit card credit assessment for existing and new customers, including EBA KPIs, SCHUFA and CRIF scorings
• Built and modeled workflows (BPMN) and decision logic (DMN) with Camunda Modeler in close collaboration with stakeholders
• Implemented service tasks, user tasks and jobs with Nest.js, Node.js and Typescript, including exception handling
• Backend-for-Frontend (BFF), frontend with React, Tailwind and Ant Design UI library
• CI/CD with GitLab, Kubernetes/Rancher

• Built and operated an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for automated deployment, monitoring and scaling of forecasting models (e.g. Temporal Fusion Transformer, Informer, Autoformer)
• Implemented CI/CD pipelines in Azure DevOps for the complete ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA clusters) through training and evaluation to model registry and endpoint deployment
• Integrated MLflow for experiment tracking, model versioning, performance monitoring and automated registration in Azure Model Registry
• Developed and containerized PyTorch training jobs with CUDA (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), centralized logging and cost tracking
• Configured security (OAuth2) and rate limiting via APIM
• Automated infrastructure provisioning and model deployment using Terraform, Helm and Azure CLI; integrated with existing market data systems and event pipelines
• Migrated existing workloads and databases (IONOS → Azure, MongoDB) integrating them into central MLOps workflows and internal networks
• Extended 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
• Analyzed and designed a software solution to efficiently process high-volume data (>3000 messages/sec) (market data store)
• Developed Spring Boot / Java 21 containers with RabbitMQ to distribute market data via MongoDB (Kubernetes) with fast data storage in Redis RMaps, deduplication, forwarding of messages to read model queues and building read models for UI display in MongoDB
• Integrated RESTHeart to generate a REST API for MongoDB
• Migrated to MongoDB ClickHouse for mass ingests and automatic deduplication using the ReplicatedMergeTree engine in ClickHouse
• Built a Python Apache Arrow Flight service for querying the ClickHouse DB in milliseconds for complex queries (gRPC protocol / ClickHouse column-based queries)
• Developed an Angular frontend to simplify data queries and master data maintenance
• Used agentic coding with remote and local LLMs (Claude, Ollama Qwen, OpenLLM) and MCP servers
• Created Python scripts for transforming and cleaning incoming market data (Pandas, scikit-learn)

• IoT platform to connect, transform and get actionable results
• Built a microservice observability solution to improve insights for developers and operations teams
• Made advanced performance optimizations in .NET code, databases and time series
• Provided architectural advice to developer teams on performance, scalability, reliability and code quality
• Evaluated and built PoCs for new technologies, including time series databases and messaging platforms
• Technologies: C#/F#/Rust, InfluxDB, ClickHouse, Prometheus, Grafana, k6, RabbitMQ, EMQX, Docker, Kubernetes, Azure AKS

• Development of a central billing app for cloud costs at DB
• AWS
• Python
• AWS CDK
• RDS
• Spark (PySpark)
• Glue
• Lambda
• CI/CD (GitLab)
• React/Typescript
• data optimization
• Scrum

• Consulting and development in a greenfield project for acquisition of sensor data.

• The new solution is meant to replace an existing system, where most of the logic is hardcoded and which is primarily targeted to the German market.

• The new system is meant to support international markets and the logic and configuration can be changed easily.

• The new system has to support multiple time zones and international requirements and regulations.

• Working closely with the in-house software architect, developing Enterprise Architect models, proof-of-concepts and implementations.

• The complete system has a generative approach, where C# code, database, dashboards and reports are generated from compact descriptions.

• The system is meant to be stable and secure. Encryption, hashing and signing are used throughout.

• After extensive performance measurements and various evaluations, we decided to use the ClickHouse database, which provides excellent performance and rather good SQL compatibility.

• Sparx Enterprise Architect, JAMA, Miro.

• ClickHouse Database, Linux / Debian / Ubuntu, GitLab.

• Docker, Cobasoft Log & LogVw.

• Microsoft: Visual Studio, C#, ASP.NET, Test.

• HTML5, CSS, JavaScript, JSON.

• Edge, FireFox, Chrome, Chromium.

• Code and HTML generation.

• Working on a project related to redundant crisis infrastructure
• Used tech: TypeScript, Node, Java, MongoDB, Kafka

• Led a team of 6 software engineers to develop and maintain an AI-driven healthcare platform, enabling automated diagnostics and prescriptions based on real-time ECG data analysis
• Designed and developed a robust Revenue Cycle Management (RCM) system, integrating HL7 and FHIR APIs to enable seamless interoperability, real-time data exchange, and HIPAA-compliant data handling, improving billing efficiency, claim processing, and regulatory adherence in healthcare operations
• Migrated a legacy monolithic application to a scalable microservices architecture, enhancing system modularity, scalability and maintainability while implementing key design patterns such as Strangler, Database-per-Service, API Gateway, Saga and CQRS for efficient service communication and transaction management
• Architected and led a C# 9/.NET 6 microservices ecosystem handling hotel reservations, payments, and loyalty programs, enabling 99.99% uptime across 10+ services
• Defined OpenAPI/Swagger contracts and auto-generated client SDKs, reducing front-to-backend integration time by 50%
• Containerized each service with Docker and orchestrated deployments via Kubernetes, slashing release lead time from days to hours
• Designed PostgreSQL schemas optimized for high-volume transactional workloads and implemented Redis caching layers to accelerate read-heavy endpoints by 80%
• Built Kafka streaming pipelines for real-time availability updates and audit logs, processing 2 million+ events per hour with end-to-end delivery guarantees
• Implemented unit and integration tests for React applications using Jest and React Testing Library, ensuring 80%+ test coverage, improving component reliability, and preventing regressions
• Defined and deployed AWS cloud infrastructure using Terraform, while containerizing and orchestrating microservices with Docker and Kubernetes, improving automation and system scalability
• Built a scalable full-stack booking application using React 18 and Django REST Framework, integrating Celery and Redis for asynchronous task processing, while deploying on GCP with Cloud Run and Firestore, enabling real-time scheduling, payment processing, and automated notifications
• Mentored junior developers through code reviews, pair programming, and knowledge-sharing sessions, improving team efficiency by 30% while maintaining comprehensive API documentation using Swagger/OpenAPI