.NET Core Experts in Germany

Product:

• Built a production-ready AI platform for generating brand-aware marketing images and videos from product data, user requirements, and uploaded media.
• Own the platform architecture, technical roadmap, API design, security, deployment workflow, operational reliability, and model-provider strategy.
• Developed the core platform in .NET and built supporting AI and workflow prototypes in Python, applying language-independent API contracts and structured interfaces between services and model providers.
• Implemented reliable background processing with RabbitMQ, persisted workflow state, idempotent handling, retries, failure recovery, logging, secure storage, authorization, and credit accounting.
• Made pragmatic build-versus-buy and model-routing decisions based on reliability, latency, cost, and maintainability rather than novelty.

Agent Orchestration & RAG Systems

• Built and compared agent workflows using Microsoft Agent Framework, LangGraph, and LangChain, including tool use, conditional routing, clarification steps, state management, and hand-offs between agents.
• Implemented reusable .NET components for agents, prompts, tools, model providers, structured responses, and retrieval with pyvector, making it easier to change AI providers without rewriting the core workflow.

• Development of scalable backend applications with C#/.NET and Java
• Design and implementation of distributed microservice architectures
• Development and integration of REST APIs for industrial applications
• Development of modern web applications with React, Angular, and TypeScript
• Implementation of MQTT-based communication solutions
• Integration of industrial protocols such as OPC UA and Modbus TCP
• Development of batch, process control, and HMI components
• Containerization and deployment of applications with Docker
• Conducting code reviews and supporting architecture decisions
• Close collaboration with Product Owners, QA, and interdisciplinary teams
• Analysis of business requirements and implementation of technical solutions
• Further development of existing software architectures with a focus on maintainability and performance

Technologies: C#, .NET, ASP.NET Core, Java, C++, React, Angular, TypeScript, Vue.JS, MQTT, OPC UA, Modbus TCP, Docker, GitLab, MariaDB, MySQL, Linux

• Designed and implemented modular AI assistants for sales and business processes within the Microsoft ecosystem (Agentic AI, Copilot extensions)
• Frontend architecture and development with React + TypeScript for embedded chat and assistant surfaces (streaming UI, Hooks, React Query, OpenAPI clients)
• Enterprise-level agent development: reusable skill/agent library, MCP servers, review and compliance gates
• LLM integration into the user experience: Anthropic (Claude), OpenAI, tool use, RAG pipelines, prompt engineering, guardrails
• Architecture and code review consulting, and mentoring within the AI development team
• Integration with Microsoft Graph, Power Platform, and Azure services
• Technologies: React, TypeScript, Anthropic Claude, OpenAI, MCP, RAG, Microsoft Graph, Power Platform, Azure

• AI Integration (LLM & RAG): Designed and built an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data to automatically analyze technical documents and error logs.
• Design & Implementation: Designed a robust RS-232/UART communication interface for an SBC-based embedded device used to control medical shaker systems.
• Architecture & Protocol Design: Built a highly maintainable software structure (OOP, SOLID) and defined resilient, hardware-level communication protocols, including multithreading and advanced error handling.
• Quality Assurance & DevOps: Automated testing with xUnit, ran integration tests directly on the hardware target, and maintained technical documentation 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

Embedded Analytics & AI-assisted BI

Design and development of an integrated analytics solution based on ASP.NET Core, Power BI Embedded, and LLM services to provide context-based business information.

Development of an AI agent with Function/Tool Calling for the secure orchestration of REST APIs, SQL data sources, and technical services within defined business processes.

Building automated BI workflows including workspace management, deployment processes, and scheduled refresh via the Power BI REST API.

Implementation of secure service-to-service communication with Microsoft Entra ID and Service Principal, and integration into existing enterprise system landscapes.

Technologies: ASP.NET Core, C#/.NET, Power BI Embedded, Power BI REST API, LLM API, AI Agents, Function/Tool Calling, Entra ID

Gronic Ident & Sign

Core technologies: Android, Jetpack Compose, Retrofit, Regula Document Reader, Adobe Sign

Android app on dedicated devices for identifications and electronic signatures at the PoS

Unified ITSM framework

• Definition of a company-wide ITSM target picture
• Introduction of a uniform service structure across all business units

SLA and OLA management

• Building a standardized SLA framework
• Definition of service classes (Business Critical, Standard, Low Priority)
• Introduction of OLAs between internal teams
• Building meaningful SLA reporting
• Definition of KPI and service dashboards for business units

Service portfolio management

• Definition of service descriptions
• If needed, preparing possible cost and service billing

Ticketing & processes

• Incident management
• Uniform ticket categories
• Standardized prioritization
• Escalation matrix
• Automations
• Self-service optimization

Request fulfillment

• Service catalog across all business units
• Approval workflows

Problem management

• Introduction of root cause analysis
• Known error database
• Problem review process

Complete asset management concept

• Hardware lifecycle management
• Software lifecycle management
• Leasing lifecycle
• Mobile device lifecycle
• Monitor lifecycle
• Phone lifecycle

Processes

• Procurement
• Goods receipt
• Inventory
• Assignment
• Return
• Disposal
• Leasing return Goal: single source of truth for all assets

CMDB design

• Definition of all configuration items:
• Workplace
• Notebooks
• Monitors
• Mobile phones
• Printers

Infrastructure

• Servers
• Firewalls
• Switches
• WLAN
• Storage
• Backup systems

Cloud

• Azure resources
• Microsoft 365
• SaaS services

Relationships

• User ↔ Asset
• Asset ↔ Service
• Service ↔ Infrastructure
• Location ↔ Asset
• Goal: make all service dependencies visible

Software asset & license management

• License management concept
• License balancing
• Compliance reporting
• Microsoft license management
• Adobe license management
• SaaS management
• Contract management
• Renewal management

Interfaces & automation Existing systems

• Workday
• Joiner
• Mover
• Leaver

TESMA

• Leasing data
• Contract data

Matrix42

• Asset synchronization
• User synchronization

Active Directory / Entra ID

• User management

Microsoft 365

• License assignment
• Group management

Dormakaba

• Access processes

• Lifecycle services

Monitoring platforms

• PRTG
• Palo Alto
• Cisco

Reporting & KPI framework

• Definition of a management dashboard
• KPIs
• Ticket volume
• SLA fulfillment
• MTTR
• First resolution rate
• Asset accuracy
• License compliance
• Change success rate
• Service availability
• Degree of automation

Network redesign support

• Governance
• Support of the network redesign from an ITSM point of view
• Definition of affected services
• Change management structure
• Communication concept

CMDB integration

• Recording of all network components
• Service mapping
• Dependency analysis

Validation of documentation and knowledge base articles

• Network documentation
• Operations documentation
• Standard changes

Monitoring & event management

• Target picture
• Central monitoring concept
• Event management process
• Alerting strategy
• Escalation model

Systems

• Cisco

• Palo Alto

• Fortinet

• Rubrik

• Veeam

• Matrix42

• Azure

• Microsoft 365 Automation

• Ticket creation from monitoring

• Escalations

• Standard actions

Audit, compliance & information security

• ISO 27001 consulting
• TISAX consulting
• NIS2 preparation - consulting
• Audit-ready processes
• Documentation structure
• Evidence tracking in Matrix42

Roadmap

• 12-month roadmap
• Prioritization of all measures
• Quick wins
• Medium-term projects
• Long-term target picture
• Documentation

AI Platform Backend – Senior Developer

Hired to design and build an AI platform backend from the ground up, including multi-provider LLM orchestration and real-time infrastructure for AI influencer personas at scale.

Tasks and Responsibilities

• Architected and implemented a multi-LLM orchestration layer with Semantic Kernel to integrate GPT-4 and other providers for core platform logic and AI influencer personas, reducing model-switching overhead by abstracting provider APIs behind a unified interface.
• Designed and developed a backend from the ground up in C# / .NET 10, including domain modeling with DDD, a versioned RESTful API layer, and cloud infrastructure on Azure.
• Built real-time chat infrastructure with Server-Sent Events (SSE), message persistence, and delivery guarantees for running AI influencer personas live at scale.
• Developed a media management service with cloud object storage integration for uploading and retrieving influencer-generated content.
• Created an integration and unit test suite with data seeding for reliable regression testing across all core platform flows, significantly reducing the production error rate.

Tools and Technologies: C#, .NET, ASP.NET Core, Python, TypeScript, MySQL, Semantic Kernel, EF Core, Minimal APIs, LLM Orchestration, Prompt Engineering, Agentic AI, Generative AI, AI-Assisted Engineering, Claude Code, GitHub Copilot, Google Gemini, OpenAI API, Ollama, Redis, Azure, Azure Container Apps, Azure Database for MySQL, Docker, GitHub Actions, Clean Architecture, Vertical Slice Architecture, CQRS, Domain-Driven Design, REST API, xUnit, Integration Testing, Unit Testing, Jira, Confluence, Scrum

• Software development with C#

• IT infrastructure development and maintenance with Microsoft Azure

• Integration of 3rd-party systems

• Planning and setting up DevOps CI/CD processes

• C# (.net8)

• Azure Cloud Services

• DevOps

• Jira

• Dynamics

• Odata

• Api Management

• Key Vaults

• ServiceBus

• Azure Functions

• Blazor

Task:

• Design and build a proof of concept (PoC) for a future-proof virtualization platform, taking secure system architectures into account
• Assess the current state of existing infrastructures and develop selection and evaluation criteria for the right OS virtualization platform
• Carry out the requirements analysis and then create and prioritize tickets in the ticket system
• Complete and continuously update a tool evaluation matrix based on PoC results
• Support team knowledge building through clear documentation of the approach and results in Confluence
• Enterprise analysis of existing hardware (creating different BoMs)

Technologies: Vmware, Vmware Aria Operations, Osism, Canonical OpenStack, FishOs, Linux, Terraform, Ansible, Confluence, Alma

• Designing the audit logging framework
• Implementing APIs for developers to integrate in their codebase
• Implementing ingestion pipeline, database query layer and UI for browsing the audit events
• Improving stability and reliability of the backend system

Development of an iOS app for virtual try-on and outfit suggestions

The goal of the project is to develop a mobile application for personalized, photorealistic outfit suggestions. Users should be able to upload their own photos, try on clothes virtually, and find products that can be bought directly in the generated suggestions.

• Planning and implementation of the onboarding and photo upload in the iOS app
• Development of the mobile application with Expo and React Native
• Implementation of a Hono/Node.js backend for user, product, and generation processes
• Building an asynchronous processing pipeline with BullMQ and Redis
• Connection of PostgreSQL/pgvector and S3 for product, image, and generation data
• Integration of Gemini and OpenAI for outfit generation and image processing
• Implementation of a credit system and integration of RevenueCat
• Integration of Stripe Connect and affiliate product feeds for products that can be bought directly

Label: TypeScript, React Native, Expo, Hono, Node.js, PostgreSQL/pgvector, BullMQ, Redis, S3, Gemini, OpenAI, RevenueCat, Stripe Connect, Docker

• Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
• Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.

• 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).