Docker Compose Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Docker Compose
Thomas H.
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).
Vitaliy R.
Last position:
DevOps GitOps (temp) at Signal Iduna
- Responsible for Openshift/Kubernetes on-prem administration and developer support.
- Developed URP infrastructure automation with Python, Ansible, Kustomize and ArgoCD, Argo Workflow/Events stack.
- Wrote smoke and load tests for URP infrastructure utilizing Python, Kustomize and ApplicationSets.
- Helped to set up and deploy URP infrastructure in Google Cloud, GKE.
- Set up monitoring for URP and ArgoCD stack with Splunk Cloud.
- Performed system administration tasks across RedHat Linux, Kubernetes/Openshift, ArgoCD, GitLab, Bitbucket Enterprise, Kafka and MongoDB.
Umut T.
Last position:
App Engineer at Tonies SE
Worked as an App Engineer as part of the IoT Control team in a React Native Stack.
- Implementing new Components based on the Tonies Design System.
- Improving, extending and maintaining Tonies Design System
- Adding new features like Volume and Playback Control of the Tonies
- Fixed connection issues via bluetooth and wifi
- Removing technical debt and polishing for an upcoming big software and hardware release
- Improving accessibility by optimizing screens and components for usage via screen readers.
- Wrote lots of automated tests to ensure app quality is always stellar.
- Weekly app releases in a high volume workload environment
Results & Impact:
- Improved app stability and connection reliability ahead of a major software and hardware release
- Raised overall app quality through a significantly broader automated test suite, catching regressions before they reached users
- Made the app usable with screen readers, improving accessibility for a wider audience
- Sustained a weekly release rhythm in a high-volume production environment without quality regressions
Technologies: react-native, expo, typescript, react-19, react-compiler, react-navigation, zustand, @tanstack/react-query, i18next, figma, app-store, play-store, sentry, bitrise, config-cat, poeditor
Christian T.
Last position:
HDI DevOps & Fullstack Engineer at HDI
- Spring Boot Software Engineer
- DevOps Engineer (Kubernetes, Azure DevOps)
Toolstack: Java, Spring Boot, Kubernetes, Helm, GitOps, ArgoCD, Docker, Azure DevOps, CI/CD Pipelines, Git, Postgres
Frederik C.
Last position:
Freelance Full-Stack Software Developer at Bundesdruckerei GmbH
Development of the digital organ donation register, commissioned by the Federal Institute for Drugs and Medical Devices (BfArM)
Implementation of user stories in several microservices (frontend and backend)
Ensuring quality with unit, integration, and E2E tests
Conducting code reviews
Coordination with other development teams
Taking over the software license check and simplifying the process
Responsibility for implementing and documenting the business logging
Setting up a development environment with Docker Compose
Esmaeil M.
Last position:
System Test & Integration Engineer (5G Core / Security / Cloud-Native) at Secuvera GmbH
- Independently carried out integration, testing, and acceptance activities on 5G core systems in a containerized test environment; structured test protocols and documentation
- Built a 5G core and RAN integration environment from scratch (Docker Compose) for reproducible test scenarios across multiple vendor configurations
- Performed fault analysis and troubleshooting across the entire protocol stack (SBI, RRC, NAS, NGAP, TLS) to precisely isolate integration issues
- Contributed to the BSI-related NESAS-CCS-GI evaluation project – experience in security-critical, regulated customer projects
Alois F.
Last position:
Software Architect / Backend Developer at GEO API
- Design and implementation of a Laravel backend API for provider-based geo search, feature details, boundary geometries and neighborhood detection.
- The API serves as a server-side proxy, cache and enrichment layer, protecting credentials for external geo services and persisting features, localizations, geometries, hierarchies and neighborhood relationships in MariaDB.
- Key areas included normalization of autocomplete and feature data, caching strategies for expensive boundary/polygon requests, spatial processing of GeoJSON and MultiPolygon geometries, as well as neighborhood detection using hierarchies, bounding box filters and topology checks.
- Additionally, separate production and diagnostic endpoints were created, along with status and error catalogs, discovery logging, a Docker development stack, API documentation, and unit and feature tests.
- Technologies: PHP 8.3, Laravel, REST API, geo data API, GeoJSON, MariaDB Spatial, Eloquent, Laravel Queues, PHPUnit, Mockery, Docker, Docker Compose, API design, geocoding, boundary data, spatial queries.
Mohamed S.
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
Boris N.
Last position:
Fullstack Developer & DevOps Engineer at EnBW Energie Baden-Württemberg
- Further development of the internal "ECockpit" platform with an Angular 17 frontend and .NET (C#) backend
- Maintenance and further development of Azure DevOps pipelines
- Introduction of technical improvements in build & release processes
- Collaboration on a modular architecture approach (Clean Architecture & DDD)
- Focus on scalability and secure data processing
- Tech stack: Angular 17, .NET / C#, Azure, Azure DevOps, Git, CI/CD, Clean Architecture, Domain Driven Design
Janusz M.
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))
Sebastian F.
Last position:
Managing Director System Administration & DevOps at Far Galaxy Networks
- Windows application migration using Windows Server 2022/2025, DHCP, Active Directory, directory trust and setup, GPO management
- Cloud service automation, firewall and network management, debugging
Discover over 15,000 top freelancers
Statistics of experts using Docker Compose
Aggregated from the professional profiles of matched freelancers.
Experience
20 years (Germany: 18 years)
Position duration
1.6 years (Germany: 5.3 years)
Positions per freelancer
11 (Germany: 12)
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Banking and Finance, Insurance
Certification focus areas
Information Technology, Operations, Product Development
Bachelor's degree or higher
100% (Germany: 85%)
Master's degree or higher
43% (Germany: 49%)
Certifications per freelancer
1 (Germany: 2)
Most common languages
German, English, Italian
Speak two or more languages
100% (Germany: 99%)
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Munich using Docker Compose
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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Docker Compose experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (100%)
- Banking and Finance (55%)
- Insurance (55%)
- Energy (45%)
- Manufacturing (45%)
- Retail (45%)
- Automotive (36%)
- Healthcare (36%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Docker Compose does
Docker Compose is a tool for defining and running multi-container applications from a YAML configuration file. It describes services, networks, volumes, environment variables and dependencies so teams can start a consistent application stack with simple commands. Compose is widely used for local development, integration testing and controlled deployments.
Services and environments
A Compose project can connect an application to databases, queues, caches, search services and supporting tools. Each service runs in its own container while the configuration defines how the pieces communicate. This makes complex environments easier to reproduce across laptops, CI runners and shared test systems.
- Define application, database and message-broker services
- Configure networks, volumes, secrets and environment variables
- Create development, testing and demonstration environments
- Manage startup order, health checks and service dependencies
Ecosystem and tooling
Strong specialists work across the Docker ecosystem, including Dockerfiles, Docker Engine, Docker Desktop and the Compose CLI. They also understand image registries, container networking, persistent storage and logging. In a delivery pipeline, Compose often connects with GitHub Actions, GitLab CI, Jenkins or cloud container services.
When companies need expertise
Companies bring in freelance specialists when a stack has become difficult to start, test or maintain. They may need a legacy setup containerised, a monorepo standardised, or a dependable integration environment created for a product team. In Munich, this can support local software, manufacturing, mobility and media teams working on-site, remotely or in a mixed model.
- Replace fragile manual setup instructions
- Separate configuration safely across environments
- Reduce conflicts between local service versions
- Prepare Compose workflows for CI and handover
What good delivery includes
A reliable Compose setup is readable, portable and deliberate about security. Professionals choose suitable image versions, keep secrets outside configuration files, define health checks and document required variables. They also control data persistence and cleanup so that local convenience does not create hidden production risks.
Choosing the right specialist
Look for someone who can explain the service boundaries and diagnose failures rather than only write a YAML file. Useful evidence includes a clear Compose repository, practical knowledge of networking and storage, and experience connecting containers to application code and CI. Ask how they would handle slow startup, unavailable dependencies, data resets and differences between Docker Desktop and Linux hosts.
Frequently asked questions
Before you brief your next project: the most common questions about Docker Compose.
Docker Compose is used to define and run applications made of multiple containers. It is especially useful for local development, integration testing, demos and repeatable service environments. A Compose file can describe application services, databases, queues, networks and persistent volumes.
Docker Compose is focused on straightforward multi-container workflows, usually on a developer machine or a controlled host. Kubernetes is designed for larger-scale orchestration, scheduling, resilience and cluster operations. Many teams use Compose for development and testing while deploying to Kubernetes or another runtime in production.
A strong Docker Compose specialist should understand Dockerfiles, container images, networking, volumes and Linux processes. Experience with CI systems, Git, application configuration, registries and observability is also valuable. Knowledge of the application stack helps them diagnose issues across service boundaries.
The right Docker Compose experience depends on the work. A small local setup may need someone comfortable with service definitions and environment variables, while a shared CI environment requires deeper knowledge of networking, health checks, security and persistent data. Ask candidates to explain comparable systems and the trade-offs they made.
Docker Compose supports remote collaboration because the environment is described as versioned configuration rather than informal setup steps. A specialist in Munich can work remotely with distributed teams or collaborate on-site when access to internal systems is required. Clear documentation and consistent Docker versions remain important in either model.
Docker Compose can run production workloads in controlled situations, especially on a single host or for smaller internal services. It does not provide the full scheduling, scaling and fault-management features of a cluster orchestrator. A specialist should assess availability, backups, security and operational ownership before recommending it.
A quality Docker Compose setup is easy to start, review and reproduce. Check whether it separates secrets, declares health checks, handles data deliberately, uses clear service names and documents required variables. It should also behave consistently in CI and provide useful logs when a dependency fails.
Before working with Docker Compose, clarify the target environments, required services, data-handling rules and expected handover. Ask whether the setup is only for local development or must also support CI, staging or production. It is also important to agree on image ownership, registry access, documentation and support after delivery.
The average hourly rate of freelancers in Munich, Germany who have used Docker Compose in their recent projects is 93 €, which corresponds to a daily rate of about 748 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Docker Compose in their recent projects, 100% hold at least a Bachelor's degree and 43% hold at least a Master's degree.
On average, freelancers in Munich, Germany who have used Docker Compose in their recent projects have 20 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 Docker Compose in their recent projects are German (100%), English (100%), and Italian (18%).
The most common industries among freelancers in Munich, Germany who have used Docker Compose in their recent projects are Information Technology (100%), Banking and Finance (55%), and Insurance (55%).
The most common business areas among freelancers in Munich, Germany who have used Docker Compose in their recent projects are Information Technology (100%), Product Development (82%), and Project Management (64%).
Main locations of FRATCH Experts, who have recently used Docker Compose
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.
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Philipp Thomaschewski
FRATCH CEO
