Azure OpenAI Service Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Azure OpenAI Service
Michael Nelz
Last position:
Senior ML Engineer, AI Engineer at Lanxess AG
- Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
- Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
- Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.
Tezcan Dilshener
Last position:
Solution Architect / Project Manager at German Football Association
- Overall responsibility for the project lifecycle from scope definition to completion
- Close collaboration with platform teams, IT leaders, and external service providers
- Application of SAFe principles and structured sprint work
- Creation of a migration roadmap with clear milestones
- Monitoring of the lifecycle: onboarding, repository migration, replication of permissions, and system tests
- Visualization of the architecture with PlantUML and Gliffy as well as documentation in Confluence
- Regular status reports and running knowledge transfer sessions
Nima Nooshi
Last position:
Co founding LLM Engineer at LLM Ventures
- Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
- Designed and implemented multi-agent AI workflows for financial and trading applications
- Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
- Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
- Led system architecture decisions across model selection, orchestration, state management, and deployment
Kerstin Burgen
Last position:
Reporting and analytics for HR at Apobank
- Designing and implementing an interactive evaluation system for top executives to rate core competencies such as goal orientation, team culture, and strategic alignment.
- Integrating control mechanisms to enforce feedback limits and store evaluations in a central system to ensure data integrity.
- Optimizing data processing for personnel development by automating the merging of various information sources for form letters.
- Implementing technical data preparation and analysis for the annual compensation comparison in the financial sector.
- Developing automated processes for data preparation in Excel using Power Query, ensuring data integrity and anonymization according to data protection requirements.
- Automating personnel cost analysis by developing a solution to process data from the Paisy system into an SAP-compatible Excel file.
- Creating test cases, user documentation, and test plans for all developed systems.
- Technologies: Power Query, MS Office 2016 (Word, Excel, PowerPoint), Paisy, SAP, VBA.
Mohamed Saleh
Last position:
Machine Learning Engineer (Part Time) at E.ON Digital Technology
- 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
Maksym Shvaykovskyy
Last position:
Head of System Integrations at TAKKT AG
- Lead high-performing teams to enhance enterprise system efficiencies, specializing in ERP and PIM systems within AWS, Azure, and proprietary datacenter infrastructures
- Ensure seamless alignment with stakeholder visions through strategic tech integration and dynamic leadership
- Build Generative AI solutions using platforms such as Azure/OpenAI, Google, Claude, and Ollama
- Develop intelligent system integrations and advanced data strategies leveraging AI and machine learning models
- Champion Agile methodologies (SCRUM) and mentor team in roles such as Team Lead, Scrum Master, and Product Owner
- Deliver robust, scalable solutions that power strategic business growth and operational excellence
Janusz Mazurek
Last position:
IoT Edge Computing / Self-Driving-Cars at Automotive consulting company
- Platform: Python ecosystem, RHEL 8, K10, AWS IoT Core, AWS Lambda, MLOps
- Software: Java JEE/cloud, IntelliJ IDEA, AWS IoT Core, AWS Edge and Lambda, AWS SageMaker SDK, Docker Compose, Kubernetes, OpenShift 4, Tekton, Flux, Helm charts, JSON/XML technology, Nginx, Apache Spark, OpenAI (GPT Plus, DALL-E 3, Whisper), GAN, GitHub Copilot, AI/machine and deep learning, Jupyter notebooks, TensorFlow 2, Colab, Keras API, Prometheus, Grafana, Conda, Python 3.9, PySci stack (NumPy, pandas, Scikit-learn, matplotlib)
- Responsible for webinar:
- IoT edge computing: architecture, components, resources, management
- IoT edge computing with MicroK8s, designing and creating flows/diagrams for AWS, three-step model for IoT ecosystem
- IoT processes, connectivity, data transfer and deployment, security
- Optimization of edge computing for IoT networks and services (AWS SQS queue, SNS notifications, events, analytics, buttons, device management/defender, Things Graph)
- Machine/deep learning frameworks (models, training, pipeline optimization, deployment in the cloud/at the edge (OpenShift), monitoring workloads with Prometheus and Grafana)
- Performance optimization for low latency/resilience using adaptive ML/DL/RL models for customer IoT data
- Analysis of large sensor data sets with Apache Spark, Kafka clusters
- Kasten K10 data management platform on Kubernetes multi-cluster with Helm chart, deployment, backup/disaster recovery (RTO/RPO), data lifecycle and security management
- Implementation of multilayer artificial neural network (ANN) with TensorFlow 2 and Colab for regression and classification; data analysis and provisioning for applications; development of models for testing and training, deployment of models
- Automation of business streamline processes with AI (Azure OpenAI, Discord bots/Zapier apps AI assistants (IntelliJ, GitHub Copilot))
Discover over 15,000 top freelancers
Statistics of experts using Azure OpenAI Service
Aggregated from the professional profiles of matched freelancers.
Experience
20 years
Position duration
1.4 years
Positions per freelancer
17
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Banking and Finance, Automotive
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
33%
Certifications per freelancer
3
Most common languages
German, English, Arabic
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Munich using Azure OpenAI Service
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it does
Azure OpenAI Service brings OpenAI models into Microsoft Azure with enterprise controls, networking options, and region-aware deployment. Teams use it to add chat, search, summarization, extraction, and content generation to internal tools and customer apps.
Common builds
- Chat assistants for employees and customers
- Retrieval-augmented search over company content
- Document summarization and classification
- Workflow automation with structured outputs
- Multilingual experiences tied to Azure data services
Ecosystem fit
Strong professionals working with Azure OpenAI Service know Azure AI Foundry, Azure Functions, App Service, Logic Apps, and Azure AI Search. They also work with identity, private networking, monitoring, and guardrails so the system fits enterprise architecture instead of feeling like a demo.
When to bring help
Companies bring in freelance experts when a proof of concept needs to move into a secure production setup. That often means prompt design, model selection, API integration, latency tuning, cost control, and access policies. In Munich, this is common for teams in industrial software, mobility, finance, and B2B services that need clear English communication and smooth local collaboration.
What strong experts do
- Design prompts and structured responses for real business tasks
- Connect models to private data with search and retrieval
- Set up evaluation, logging, and safe fallback paths
- Work with Azure identity, security, and deployment constraints
- Improve reliability after launch, not only at kickoff
Delivery signs
A strong Azure OpenAI Service specialist can explain where the model fits, where it should not be used, and how the app will behave under load. They should be able to discuss token use, data boundaries, hallucination risk, and human review without vague language. That is what separates a real production expert from a surface-level integrator.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Azure OpenAI Service.
Azure OpenAI Service is used to add language model features to business systems inside Azure. Common work includes chat assistants, search over private content, text summarization, extraction, and content drafting with enterprise controls.
Azure OpenAI Service is often chosen when a company wants OpenAI models with Azure-native security, networking, and governance. Direct OpenAI use can be simpler for small apps, while Azure OpenAI Service usually fits better when the rest of the stack already lives in Azure.
A strong Azure OpenAI Service specialist usually also knows Azure AI Search, identity and access control, API design, and basic cloud deployment. Prompt design, evaluation, logging, and data handling matter just as much as calling the model endpoint.
It depends on risk and scope, but production work usually needs someone who has shipped model-backed features before. The best Azure OpenAI Service experts can handle security, reliability, and user experience together, not just the first prototype.
Yes, most Azure OpenAI Service work can be done remotely if access, reviews, and communication are set up well. For Munich teams, a hybrid setup can help when workshops involve business stakeholders, security reviews, or existing Azure architecture discussions.
Ask how the expert would choose models, protect data, and measure answer quality. A good Azure OpenAI Service professional should also explain fallback behavior, approval flows, and how they would reduce bad outputs in your specific use case.
It works well when a company needs controlled access to generation inside an existing Azure environment. Azure OpenAI Service is a strong fit for internal knowledge assistants, document workflows, support tooling, and multilingual business applications.
Look for concrete examples of production work, not just prompt ideas. A solid Azure OpenAI Service specialist talks clearly about architecture, cost control, evaluation, security, and what they would ship in the first version versus later improvements.
The average hourly rate of freelancers in Munich, Germany who have used Azure OpenAI Service in their recent projects is 101 €, which corresponds to a daily rate of about 805 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Azure OpenAI Service in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Munich, Germany who have used Azure OpenAI Service in their recent projects have 20 years of professional experience, with a single engagement typically lasting around 1.4 years.
The most common languages among freelancers in Munich, Germany who have used Azure OpenAI Service in their recent projects are German (100%), English (100%), and Arabic (14%).
The most common industries among freelancers in Munich, Germany who have used Azure OpenAI Service in their recent projects are Information Technology (100%), Banking and Finance (71%), and Automotive (57%).
The most common business areas among freelancers in Munich, Germany who have used Azure OpenAI Service in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (86%).
Main locations of FRATCH Experts, who have recently used Azure OpenAI Service
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Countries:
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
