Azure CLI Experts in Germany

• 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).

• Further development and partial new development of a distributed cloud application for providing data in energy trading
• Independent implementation of the infrastructure (IaC)
• Creation of automated CI/CD pipelines, containers, REST APIs, and Lambdas
• Continuous cost and performance optimization
• Collaboration in an international Scrum team and with experts in energy trading
• Technologies: C#/ASP.NET Core; EF Core; PostgreSQL; Terraform/AWS CloudFormation; HTTP; REST/Web API; Swagger/OpenAPI; Azure DevOps; Rider; VS Code; git; Docker; AWS (S3, EC2, Fargate, Lambda, Step Functions, Secrets Manager, VPCs, ALB/NLB, RDS, Cloudwatch, AWS CLI, Amazon MQ); Redis; OAuth; OpenID Connect
• Languages: C#; TypeScript; HCL; Docker; Bash; PowerShell; YML; JSON

Conducting a gap analysis to assess existing security measures → identifying critical vulnerabilities

Developing a catalog of measures considering risk and cost-effectiveness

Implementing an IT maturity model according to BSI guidelines

Advising management on compliance, governance, and security strategy

Establishing internal processes for a sustainable security organization

• Incident Management
• User management: creating, maintaining, and deactivating users as well as maintaining roles
• Updates and upgrades: planning and executing updates and upgrades
• Operations: performance analysis, daily monitoring, and applying SNotes
• Job management: creating, maintaining, and monitoring jobs
• Training of employees
• Development and extension of reports
• Development and extension of universes
• Technologies: SAP BusinessObjects (SAP BO 4.3), Oracle SQL Developer, SAP Information Design Tool, Octane
• Methods: Scrum

• Designed and implemented an advanced, agentic RAG pipeline using LangChain and LangGraph for structured data extraction from PDFs, utilizing tools, state management, and OpenAI LLMs (GPT-4) to improve accuracy and handle complex document structures.
• Developed a Google AI agent for extraction of structured information from PDF documents and deployed the agent on Vertex AI.
• Architected data pipelines using Azure Data Factory and Databricks to ingest data from Azure Blob Storage, process it with PySpark, and load it into Azure SQL Database via Linked Services.
• Containerized AI agents and services using Docker for consistent local development and deployment.
• Utilized PySpark and Dask for database querying in coordination with Azure Blob Storage and Document Storage.
• Created a ReAct agent that extracts structured data from PDF documents using tools and integrating Azure Document Intelligence.
• Contributed to the CPO invoices validation check project using Databricks to find existing CDRs and calculate total valid costs.
• Developed a conversational AI agent (chatbot) with a FastAPI backend, integrating RAG for precise tariff extraction and deployed the service using Azure Container Apps.
• Tools used: Azure, Azure OpenAI, Azure Document Intelligence, Azure Blob Storage, Google ADK, Google Cloud, Vertex AI, Gemini, Databricks, LangChain, LlamaIndex Ollama, Docker, PySpark, Azure SQL, Azure Data Factory, Azure AI Agent, Microsoft SQL Server

• Mercedes Me is an app that connects your phone to several features in the car
• Implemented analytical KPIs for the Digital Drivers Log (Fahrtenbuch) feature
• Used PySpark on Databricks