Ollama Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Ollama
Fred Hauschel
Last position:
Software Architect and Developer at Personal project
A recurring problem in my own AI-supported projects: requirements analysis, use cases, and architecture decisions can be created quickly with AI support, but they remain hard to trace and scattered across Markdown files – knowledge is lost as soon as it is no longer in the context window. arknet turns requirements engineering and architecture knowledge into structured, verifiable data instead of plain text: requirements, use cases, and architecture decisions as a continuously linked knowledge graph, traceable from the requirement to the architecture decision – queryable for both people and AI agents alike. Technically based on RDF/OWL and its own MCP server.
Result: MCP daemon running, Docker image automatically published on GHCR, nine hexagonal modules, eleven ADRs (including an open-core licensing model). Requirements engineering and ubiquitous language hexagon active. Publicly available since 07/2026 as a Community Edition under Apache-2.0 (github.com/kogn-io/arknet), together with the Claude Code plugin and the GHCR image; open-core model.
Label: Java, Maven, RDF, RDF4J, OWL, SPARQL, Model Context Protocol, Spring AI, Docker, GitHub, Git, Claude Code, Obsidian, DDD, Hexagonal Architecture, ArchUnit, JUnit, AssertJ
Thies Schneider
Last position:
Spatial UX Lead at govar
- Concept, interaction and UX for XR experiences for the automotive industry
- Optimizing XR experiences
- Building experiences with AI
Thomas Hoefkens
Last position:
Senior MLOps, DevOps Engineer at Trianel Energy
- 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).
Thomas Langer
Last position:
Consultant for AI-driven process automation at Lumiz
AI-driven automation of purchasing on a printing company's website, including selecting delivery times, order options, ordering, payment, and uploading print data from the Lumiz Cloud.
Andreas Blum
Last position:
Project Lead, Digital Transformation at SV Linde Tacherting e.V.
Researched, developed, and implemented comprehensive digital strategy to modernize and accelerate processes of sports club with approximately 1300 members.
System Architecture & Implementation: Conceived and set up central cost- and energy-efficient ARM-based server infrastructure.
Selected, installed, and configured open-source solutions for knowledge management, ticket booking, and member management.
Ilian Sapundshiev
Last position:
Project Manager at AutoScout24 GmbH
- Successfully migrated over 150 BI dashboards (Jira, Microstrategy, AWS QuickSight) as project lead of 4 developers, ensuring a smooth transition and maintaining data integrity.
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
Markus Binder
Last position:
Technical Co-Founder at Loka AI
- Software development of a B2B SaaS for AI-based search in internal candidate pools of recruitment agencies
- Design of a multi-tenant, hybrid architecture with dedicated GPU servers and secure cloud integration
- AI-Engineering
- LLMOps
- Python
- FastAPI
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
Discover over 15,000 top freelancers
Statistics of experts using Ollama
Aggregated from the professional profiles of matched freelancers.
Experience
21 years (Germany: 19 years)
Position duration
1.6 years (Germany: 2.9 years)
Positions per freelancer
14 (Germany: 13)
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Automotive, Insurance
Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
100% (Germany: 96%)
Master's degree or higher
100% (Germany: 69%)
Doctorate
29% (Germany: 13%)
Certifications per freelancer
1 (Germany: 3)
Most common languages
German, English, Spanish
Speak two or more languages
100% (Germany: 97%)
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 Ollama
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
Local LLMs
Ollama is used to run large language models locally on a laptop, workstation, or server. Companies use it when they want private AI, lower latency, or more control over what leaves their systems. It is a practical fit for internal assistants, search, and drafting tools.
What specialists deliver
- Set up Ollama for local inference and model management
- Choose suitable models for chat, summarization, or extraction
- Connect Ollama to internal apps, APIs, and tooling
- Tune prompts, context size, and response behavior
- Package deployments for teams that need repeatable setups
Ecosystem fit
Ollama often appears with tools such as OpenAI-compatible APIs, Python services, Docker, and vector databases. Specialists know how to pair it with models like Llama, Mistral, or Gemma, then shape the full workflow around retrieval, chat, or document handling. The best professionals think beyond the model and into the whole path from input to output.
When companies bring in help
Teams usually need freelance expertise when a proof of concept must become a usable internal tool. In Munich, this often matters for software, industrial, and mobility teams that want local AI without sending data to external services. Strong specialists can move quickly from setup to a stable pattern for testing and handover.
Signs you need an expert
- The model runs, but responses are inconsistent
- You need private processing for sensitive documents
- Your team wants a clean way to swap models
- You must integrate with an existing service stack
- The setup needs to work for more than one machine
What strong professionals do
A strong Ollama specialist understands model behavior, not just installation. They can explain why one model fits a task better than another, and they know how to keep prompts, memory use, and output quality under control. They leave behind clear setup notes so teams can maintain the system without guesswork.
Frequently asked questions
Questions about Ollama? Start with the answers below.
Ollama is used to run language models locally for chat, document work, extraction, and internal assistants. Companies choose it when they want more control over data and model behavior than a hosted service usually gives. It is a good fit for internal tools that need to stay close to company systems.
Ollama gives teams local control, while hosted APIs trade that control for convenience and external infrastructure. The right choice depends on privacy needs, latency targets, and how much control you want over model selection. Many teams use Ollama for internal work and hosted APIs for public-facing features.
A strong Ollama specialist usually knows model selection, prompt design, API integration, and local deployment basics. Useful adjacent skills include Python, Docker, retrieval workflows, and simple backend service design. If the project touches internal knowledge search, vector databases also matter.
A small proof of concept may only need a specialist who can install Ollama, test a few models, and connect one workflow. A production setup needs someone who can handle stability, security, logging, and handover. The more users and data sources you add, the more important deep experience becomes.
Yes. Most Ollama work can be done remotely because setup, model testing, and integration usually happen in code and on controlled machines. On-site time in Munich can help when teams need access to internal systems, shared infrastructure, or fast workshops with product and security stakeholders.
Ollama is often used with models such as Llama, Mistral, and Gemma. The best choice depends on the task, hardware limits, and the language or domain the team cares about. A good specialist will test a few options rather than picking one by habit.
Look for evidence of real model work, not just installation notes. A good Ollama professional can explain model trade-offs, show a clear integration path, and describe how they checked output quality. Strong signs are clean documentation, stable handover, and practical advice on what to keep simple.
Yes, Ollama is often chosen for private document assistants and internal search tools. It can be combined with retrieval so answers are grounded in company content instead of only the base model. That makes it useful for teams that want local processing and controlled access to sensitive files.
The average hourly rate of freelancers in Munich, Germany who have used Ollama in their recent projects is 92 €, which corresponds to a daily rate of about 739 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Ollama in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 29% hold a doctorate.
On average, freelancers in Munich, Germany who have used Ollama in their recent projects have 21 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Munich, Germany who have used Ollama in their recent projects are German (100%), English (100%), and Spanish (22%).
The most common industries among freelancers in Munich, Germany who have used Ollama in their recent projects are Information Technology (100%), Automotive (67%), and Insurance (56%).
The most common business areas among freelancers in Munich, Germany who have used Ollama in their recent projects are Information Technology (100%), Product Development (100%), and Project Management (78%).
Main locations of FRATCH Experts, who have recently used Ollama
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.
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Berlin Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
