YAML Experts in Munich
with precise AI matching from over 15,000 CVsHire experts who create maintainable configuration for Kubernetes, CI/CD pipelines and infrastructure automation, while connecting YAML with JSON, Git and cloud tooling. FRATCH matches you quickly with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts in Munich, who have recently used YAML
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Markus B.
Last position:
Developer / DevOps, Consulting at A state authority in Baden-Württemberg / FSAS Technologies GmbH
Oracle Project Management and Automation
As part of the project, the operation of an Oracle infrastructure was automated by developing and enhancing Puppet modules and migrating existing Bash scripts to a standardized configuration management solution.
The aim of the project was to standardize and automate administrative processes, improve the maintainability of the Oracle platform, and increase the efficiency and traceability of infrastructure management through the use of Infrastructure as Code.
- Analysis and migration of existing Bash shell scripts to a Puppet-based automation solution
- Development and enhancement of Puppet modules for Oracle environments
- Automation of installation, configuration and operational processes using Infrastructure as Code
- Standardization and optimization of configuration management for Oracle systems
- Creation and maintenance of technical and operational documentation
- Planning, coordination and professional support during project implementation
- Operation and automation of Oracle systems on x86-based Linux server platforms
- Testing and quality assurance of the developed Puppet modules
- Analysis and resolution of technical errors as part of the automation and migration
- Close coordination with specialist departments and infrastructure teams to implement automation requirements
Technologies used: Puppet 7/8, SLES 15, Oracle 19, Oracle Grid, SVN, Windows Server, Confluence, VS Code, Ruby, Bash, Pass
Daniel R.
Last position:
Software Engineer at DB InfraGO AG
- Development of dynamic web components for displaying KPIs, intelligent map applications, and operational process analysis tools
- Angular 18+
- Leaflet, MapLibre
- NestJS, JavaScript, HTML, CSS
- PostgreSQL, GraphQL, RabbitMQ
- Gitea, Jenkins, Docker
Krithika C.
Last position:
Professional Reorientation at Von Rundstedt
- Engaged in a structured career development program while strengthening German language proficiency (B1 level) and evaluating opportunities in ADAS/AD systems and requirements engineering.
Maziyar K.
Last position:
Data Engineer at MSD Germany
- Lead Architect to design and implement the data lake and ETL Pipeline using AWS Stack
- Performance Optimization of Data Ingestion of ETL Pipeline
- Development of Data Validation using Great Expectations
- Leading of the data migration for two sources exchanges
- Data Modeling in AWS Redshift
MLOps
- Model inference implementation by mlflow and AWS SageMaker
- Feature Engineering for the running ML Models ( Recommender Engineer, Clustering )
- Implementatino of Model Registry and artifactory using mlflow
- Historization an Profiling of the Input Data Using AWS Glue Crawler and AWS Data Catalog
- Feature importance using mlflow
Tech. Stack: Python 3, AWS Glue, AWS Step Fucntion, AWS Lambda, AWS EventBridge, AWS IAM Role, AWS SageMaker, AWS EC2, AWS Glue Crawler, AWS CloudWatch, MLFlow, ETL, Data lake, GitHub Action, Terraform, Jenkins, Ansible playbooks (Infrastructure as Code), CI/CD, GitLab, SQL, PySparkSCRUM, Agile, Jira, BigData, VSCode, DBeaver, MSSQL, MySQL, grafana, Docker, Linux, Bash, MapReduce, Data Modeling (ORM), Pandas, YAML, SQL-Alchemy
Daniel C.
Last position:
Founder & Managing Director at BotCraft GmbH
- Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
- Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
- Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
- Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
- Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)
Bela B.
Last position:
Full Stack Lead Developer, Backend Architect at Telefonica (O2)
The software supports the complete planning and approval of antennas for mobile telephony.
The system was implemented using an event-driven microservice architecture for cloud-native deployment with Quarkus on the backend, Kafka for communication, and Angular for the frontend. Services run on Kubernetes in Google Cloud. A special challenge was synchronizing with the legacy system still used by some users.
Valentin P.
Last position:
Student in Cloud Engineering at IAV GmbH
- Container orchestration with Docker and Kubernetes clusters
- Operating cloud platforms like Rancher, Azure, and AWS
- Development with and use of infrastructure-as-code tools like Terraform, Kubectl, and Helm
- Analysis and use of Python, YAML, and JSON
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))
Dominik A.
Last position:
IUeIvnOteprsnEatnigoinnaeleerHForechelsacnhcueler GmbH
- Migrated DataRobot into existing infrastructure
- Created AI infrastructure on AWS
- Migrated Bitbucket pipelines to GitLab
Discover over 15,000 top freelancers
Statistics of experts using YAML
Aggregated from the professional profiles of matched freelancers.
Experience
18 years
Position duration
2.1 years (Germany: 1.9 years)
Positions per freelancer
15 (Germany: 14)
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Automotive, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100% (Germany: 95%)
Master's degree or higher
88% (Germany: 63%)
Doctorate
25% (Germany: 13%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
German, English, French
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 YAML
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.
YAML 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%)
- Automotive (60%)
- Banking and Finance (50%)
- Manufacturing (50%)
- Government and Administration (50%)
- Telecommunication (50%)
- Healthcare (40%)
- Professional Services (40%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Configuration Language
YAML is a human-readable data serialization language used to express structured information through indentation, mappings and lists. Teams use YAML files for application settings, deployment definitions, automation workflows and data exchange. The name originally referred to Yet Another Markup Language and later to YAML Ain’t Markup Language, although YAML is now the standard short form.
Where YAML Runs
YAML is common wherever configuration must be reviewed, versioned and changed safely alongside source code. Its structure fits cloud-native operations, software delivery and infrastructure management.
- Kubernetes manifests and Helm values
- GitHub Actions, GitLab CI and Azure Pipelines workflows
- Docker Compose service definitions
- Ansible playbooks and inventory data
- Application configuration for frameworks and services
Ecosystem And Syntax
Strong YAML practice includes understanding indentation, scalar types, anchors, aliases and multi-document files. Professionals often work with schema validation, linters and formatters, plus JSON conversion when systems require stricter machine interchange. Git provides review and change history for YAML used as operational code.
When Expertise Helps
Companies bring in freelance YAML specialists when configuration has become difficult to review, produces inconsistent environments or fails during deployment. Expertise is also useful during cloud migrations, CI/CD redesigns and the standardization of reusable templates across teams. In Munich, this work may involve remote collaboration or on-site coordination with product, infrastructure and security groups.
Practical Deliverables
A specialist can audit existing files, define conventions and turn scattered settings into reusable, tested configuration. Typical deliverables include validated Kubernetes manifests, safer pipeline workflows, Helm chart values, Ansible structures and documentation for teams that maintain them. The work should reduce ambiguity rather than hide complexity behind clever syntax.
Quality Signals
Reliable YAML professionals understand the systems that consume the files, not only the notation itself. They test parser behavior, quote values when type interpretation could change meaning and keep secrets outside version-controlled configuration. They explain trade-offs clearly, use linters and schema checks, and make changes that remain readable for the next person who operates the system.
Frequently asked questions
Everything clients usually want to know about YAML, in one place.
YAML is mainly used for configuration and structured data. Companies rely on it for Kubernetes resources, CI/CD workflows, Docker Compose files, Ansible automation and application settings that need to remain readable in version control.
YAML is usually easier for people to read and supports comments, anchors and nested structures with less punctuation. JSON has stricter syntax and broad machine interoperability, while TOML is often simpler for smaller configuration files; the right choice depends on the consuming tool and validation requirements.
A strong YAML specialist should understand Git, schema validation and the tools that parse the files. Kubernetes, Helm, Docker, Ansible, CI/CD systems, shell scripting and cloud infrastructure are common adjacent skills because YAML rarely operates in isolation.
The required level depends on the systems behind the files, not on YAML syntax alone. A straightforward workflow edit may need focused configuration knowledge, while a Kubernetes or infrastructure redesign calls for a professional who can reason about deployment behavior, security and environment differences.
YAML work is often well suited to remote collaboration because files, reviews and validation checks can be shared through Git. On-site work in Munich can still help when the specialist must coordinate closely with local operations, security or delivery teams, but clear documentation and review practices are usually more important than location.
YAML can be deceptively sensitive to indentation, quoting and implicit data types. Duplicate keys, inconsistent conventions, hidden overrides and unvalidated templates can create failures that appear only during deployment, so the specialist should inspect both the file and the parser or platform consuming it.
Look for YAML examples that are readable, validated and connected to real operational outcomes. Ask how the professional handles schema checks, secrets, environment-specific values, reusable templates and parser differences, then review whether proposed changes are tested and easy for the internal team to maintain.
YAML was originally expanded as Yet Another Markup Language, but the project later adopted YAML Ain’t Markup Language to emphasize that it is a data serialization language rather than a document markup format. Both names refer to the same technology, while YAML is the form used in current documentation and tooling.
The average hourly rate of freelancers in Munich, Germany who have used YAML in their recent projects is 108 €, which corresponds to a daily rate of about 862 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used YAML in their recent projects, 100% hold at least a Bachelor's degree, 88% hold at least a Master's degree, and 25% hold a doctorate.
On average, freelancers in Munich, Germany who have used YAML in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Munich, Germany who have used YAML in their recent projects are German (100%), English (90%), and French (20%).
The most common industries among freelancers in Munich, Germany who have used YAML in their recent projects are Information Technology (100%), Automotive (60%), and Banking and Finance (50%).
The most common business areas among freelancers in Munich, Germany who have used YAML in their recent projects are Information Technology (100%), Product Development (80%), and Project Management (70%).
Main locations of FRATCH Experts, who have recently used YAML
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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Cologne Request a free demo
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Philipp Thomaschewski
FRATCH CEO
