Terraform Experts in Munich
in minutes from over 15,000 CVs with the power of AI.Hire experts who design reusable Terraform modules, manage cloud infrastructure as code, and harden CI/CD workflows for safe changes. They work across AWS, Azure, and Google Cloud, plus state, policy, and secret handling. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used Terraform
Giuseppe Abrignani
Last position:
Embedded Software Developer at Inheco
- AI Integration (LLM & RAG): Design and build of an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data for the automated analysis of technical documents and error logs.
- Design & Implementation: Design of a robust RS-232/UART communication interface for an SBC-based embedded device to control medical shaker systems.
- Architecture & Protocol Design: Implementation of a highly maintainable software structure (OOP, SOLID) and definition of hardware-close, resilient communication protocols including multithreading and advanced error handling.
- Quality Assurance & DevOps: Test automation using xUnit, integration tests directly on the hardware target, and maintenance of technical documentation according to strict medical technology standards via Azure DevOps.
Label: C#, .NET, LLMs, RAG, n8n, RS-232, UART, Multithreading, async/await, xUnit, gRPC/protobuf, Blazor, MudBlazor, EF Core, Visual Studio 2026, Azure DevOps
Mirza Klimenta
Last position:
Agentic AI for a DeepResearch project at Freelance
- Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
- Used multiple experts (OpenAI models) collaborating during document drafting
- Extracted useful information from the knowledge graph
- Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
- Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
- Deployed initial application as a Streamlit app
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).
Mohamad Dib-Skhni
Last position:
DevOps Engineer & IT-Security-Architect at BMW Group
- Set up Azure Kubernetes clusters (AKS) with network policies, security groups, and RBAC
- Developed Terraform-based infrastructure as code for secure, reproducible deployments in the BMW Azure cloud
- Hardened CI/CD pipelines using Jenkins, SonarQube, Fortify SSC, and Contrast AST
- Integrated SAP BTP/Kyma and ServiceNow GRC
Kai Pankatz
Last position:
Oracle Project Management and DBA at Scope Solutions AG
- Installation, maintenance and regular upgrade of the DB systems with 19x
- Use of OPatch and RU 19.27 on RHEL 8x, SLES 15.x and Windows
- DBA for DACH and European customers in production and last test environments (e.g. Konrad Adenauer) as well as others in CDB
- Processes via Confluence
- Support in operations for customers SR via Metalink
Srinivasu Kakaraparti
Last position:
Atruvia
Project: Tax Exemption Order Application
The client has an existing application for creating and maintaining tax exemption orders for end customers; design and implementation of a comparable application for internal employees.
- Design and implementation of microservices and the UI for the business area "tax exemption orders" using Domain Driven Design as well as Spring Boot and Angular.
- Implementation of reactive, non-reactive, and asynchronous APIs (Spring REST, WebFlux, GraphQL).
- Development of the Angular application, including state management using Signals, RxJS Observables, and subscriptions.
- Securing the API and the application using OAuth2, JWT, and OpenID Connect.
- Configuration and setup of CI/CD pipelines with Jenkins.
- Collaboration with cross-functional teams and conducting code reviews.
Environment: Java, Spring Boot, Angular 18 & 19 (standalone, signals), RxJs, Bootstrap CSS, Vitesting, OpenShift, Istio, microservices, Kafka, Dynatrace, Jenkins, GitLab, Graylog, Sonar, Oauth2, OracleDB
Omar Ashour
Last position:
Senior Fullstack AI Engineer (Team Lead – B2C Platform) at mama health
- Partner directly with C-level leadership (CEO, CAIO, CTO) on architecture, OKR strategy, and cross-team roadmap prioritization, translating strategic goals into structured engineering requirements.
- Surfaced and mapped technical debt across the entire organization with C-level leadership and co-defined a prioritized remediation strategy, balancing debt paydown against feature delivery.
- Led code reviews and technical standards across the team, fostering a mentor-first environment with two-way feedback dialogue — pairing on complex pipeline work and unblocking junior engineers on async architecture patterns.
- Re-architected the AI companion's core processing pipeline from synchronous to asynchronous with a queue-based worker architecture, enabling horizontal scalability and cutting upload processing time ~4x (from ~22s to 5–10s) while improving response accuracy.
- Designed an AI-driven document intelligence workflow with automatic multi-document classification, per-document summarization, and relevance guardrails for the patient care journey.
- Built a unified patient memory system (short- and long-term context) bridging the document vault and chatbot into a single bidirectional, context-aware platform.
Ronald Mazelisz
Last position:
DevOps Consultant at M.it services & systems GmbH
- Adjusting, optimizing, configuring, and administering a multi-stage GitLab instance with over 250 users
- Building, adjusting, expanding, and optimizing infrastructure, configuration, and monitoring
- Providing services and handing them over to production
- System environment: DependencyTrack, GitLab, Grafana, Hedgedoc, Kubernetes, OAuth2 Proxy, Openstack, Prometheus, Syseleven
Valery Khamenya
Last position:
Sr. Data Scientist & Engineer at Virtual Minds
- Development of high-performance ad distribution via auction
- Holistic (multi-campaign & multi-channel) advertisement placement optimization
- Algorithmic optimization for NP-Hard/NP-e
- Multiple Knapsack Problem with constraints
- Online estimation of parameters in stochastic environments
Tools: Python, R, Kotlin, MILP/SAT/CP Solvers, Pytorch, Pandas, Docker
Serge Kalinin
Last position:
MLOps (machine learning operations) at REWE Digital GmbH
- It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
- GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
- Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
- CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
Michael Thomas
Last position:
Senior Freelance Software Engineer — Enterprise Software & Data Projects
- Delivered backend systems, data processing solutions, and software integrations for enterprise business applications.
- Designed and implemented API-based services connecting internal platforms with external systems.
- Built automated processing workflows to handle large-scale structured business data.
- Improved application performance by 30–50% through database optimization, caching strategies, and backend refactoring.
- Reduced manual operational effort by 40–60% by automating repetitive workflows.
- Supported production environments through troubleshooting, monitoring improvements, and continuous optimization.
- Authored technical documentation and led knowledge-transfer sessions to support long-term maintainability.
Hardeep Bhutter
Last position:
Sr. Data Engineer at Charles Schwab Bank
- Designed and implemented end-to-end data pipelines (batch & streaming) using Python, SQL, and Apache Spark, Databricks on AWS reducing ETL latency by 40%.
- Developed serverless event-driven ingestion pipelines using AWS Lambda and SQS, ensuring real-time data availability for downstream analytics.
- Leveraged Google Cloud Platform (GCP) services including BigQuery and Dataflow to manage cross-cloud data warehousing and analytics integration.
- Expertise in DMS (CDC, Full Load) and Airflow for scalable data pipeline automation and orchestration.
- Managed and customized data pipelines using Databricks, Airflow. Automation using Docker, Kubernetes, Terraform.
- Automated data quality checks using dbt to modularize transformations and ensure production-grade data lineage, improving reliability by 30%.
- Collaborated with compliance teams to ensure GDPR and SOC2 alignment. Mentored junior engineers and contributed to architecture refactoring for scalability.
- Created and maintained dashboards in Power BI to provide actionable insights.
Vitaliy Ryumshyn
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.
Tobias Nawa
Last position:
Enterprise & Solutions Architect
- Building an independent enterprise IT setup — cloud strategy, network, AWS landing zone, security requirements, contract negotiations.
- Migration of all applications; avoiding high contractual penalties for the client.
- Onboarding and coordination o...
Paul Webster
Last position:
Agentic AI Solution Architect at Solvd GmbH
As the Solution Architect for Agentic AI in auto claims processing, I led global customer delivery implementations, encompassing solution design and detailing, multi-tenancy, process flows, integration with third-party solutions, and localization requirements.
- Architectural Analysis: Conducted in-depth analysis of business requirements, managing requirements and creating detailed specifications.
- Service Definition: Developed comprehensive technical definitions for services and integration contracts.
- AI Process Management: Automated AI process management, focusing on analysis, optimization, and continuous improvement.
- Requirements Gathering: Facilitated requirement-gathering sessions and analyzed business processes to identify optimization opportunities.
- Agile Collaboration: Employed agile methodologies, working closely with stakeholders to ensure alignment and responsiveness.
- Technical Support: Assisted senior management with technical analyses and deliverability assessments.
Discover over 15,000 top freelancers
Statistics of experts using Terraform
Aggregated from the professional profiles of matched freelancers.
Experience
19 years (Germany: 16 years)
Position duration
2 years
Positions per freelancer
13 (Germany: 11)
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Banking and Finance, Automotive
Certification focus areas
Information Technology, Product Development, Business Intelligence
Bachelor's degree or higher
98% (Germany: 90%)
Master's degree or higher
78% (Germany: 56%)
Doctorate
23% (Germany: 8%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
English, German, Spanish
Speak two or more languages
96% (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 Terraform
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
Infrastructure as code
Terraform turns cloud and platform setup into code. Teams use it to create and change networks, clusters, databases, IAM roles, DNS, and other cloud services in a repeatable way. It fits new builds, migrations, and cleanup work when manual clicks are too risky.
What specialists deliver
- Reusable modules for common infrastructure patterns
- Safe plans and controlled rollouts for cloud changes
- State management and backend setup
- Policy, secret, and access handling around deployments
Strong specialists know how to keep configurations clear, predictable, and easy to review. They also understand how Terraform works with the wider delivery pipeline, not just the code itself.
Ecosystem and tooling
Terraform usually sits inside a broader toolchain with Git, CI/CD, cloud services, and secrets managers. In practice, experts work with providers, modules, remote state, workspaces, and policy checks. They also know when to use plain Terraform and when to add wrappers or supporting tooling.
When companies bring help
Companies often need freelance expertise when infrastructure has grown messy, changes feel slow, or multiple teams touch the same environment. A specialist can rescue a broken state, standardize modules, or prepare a cloud landing zone. In Munich, this often matters for teams that need clear collaboration between local stakeholders and remote delivery.
Good signs of quality
A strong Terraform professional writes code that other people can read and extend. They separate modules well, limit duplication, and think about drift, locking, and rollback paths before problems appear. They also explain trade-offs clearly, which helps product, security, and operations teams stay aligned.
Common use cases
Terraform is used for AWS, Azure, and Google Cloud setups, Kubernetes infrastructure, network provisioning, and repeatable environment builds. It is also common in platform engineering, internal developer platforms, and multi-account cloud setups. For companies, the main value is control: one source of truth for infrastructure that can be reviewed, tested, and changed with confidence.
Frequently asked questions
Not sure where to start with Terraform? These answers cover the essentials.
Terraform is used to define and manage infrastructure as code. Teams rely on it for cloud accounts, networks, access rules, Kubernetes resources, and other repeatable infrastructure work. It is a good fit when manual setup has become hard to track or easy to break.
Terraform is cloud-agnostic, so one workflow can cover multiple providers through a shared language and provider model. CloudFormation is tied to AWS, while Pulumi uses general-purpose programming languages instead of Terraform's declarative approach. The best choice depends on how much standardization, flexibility, and provider coverage a team needs.
A strong Terraform specialist usually knows modules, state, backends, providers, and plan-driven change management. Cloud knowledge matters too, especially around IAM, networking, and security boundaries. Good communication is important because the work often affects several teams at once.
Terraform help is valuable when modules are inconsistent, state is fragile, or cloud changes are slowing delivery. Freelance specialists can also help with migrations, landing zones, and cleanup after fast growth. If the team can write simple resources but struggles with architecture and governance, outside expertise helps.
Yes. Terraform is commonly used across AWS, Azure, and Google Cloud because it relies on providers rather than one vendor-specific workflow. That makes it useful for companies with mixed cloud estates or a plan to avoid lock-in.
Terraform work is often remote-friendly because most of it happens in code review, planning, and collaboration tools. On-site time in Munich can still help when infrastructure decisions involve security, architecture, or several local teams. Many companies use a hybrid setup for that reason.
Look at the structure of their modules, how they handle state, and whether they design for safe change. A good Terraform freelancer also documents assumptions, keeps naming consistent, and avoids fragile one-off patterns. If they can explain drift, locking, and provider limits clearly, that is a strong sign.
Terraform is the product name people use in day-to-day conversation, while HashiCorp Terraform refers to the vendor and full official name. In practice, both usually mean the same infrastructure-as-code tool. Searchers may use either term, so a good specialist should understand both.
The average hourly rate of freelancers in Munich, Germany who have used Terraform in their recent projects is 103 €, which corresponds to a daily rate of about 825 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Terraform in their recent projects, 98% hold at least a Bachelor's degree, 78% hold at least a Master's degree, and 23% hold a doctorate.
On average, freelancers in Munich, Germany who have used Terraform in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Munich, Germany who have used Terraform in their recent projects are English (96%), German (93%), and Spanish (27%).
The most common industries among freelancers in Munich, Germany who have used Terraform in their recent projects are Information Technology (100%), Banking and Finance (60%), and Automotive (56%).
The most common business areas among freelancers in Munich, Germany who have used Terraform in their recent projects are Information Technology (100%), Product Development (80%), and Project Management (58%).
Main locations of FRATCH Experts, who have recently used Terraform
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 
Cologne 
Frankfurt 
Stuttgart 
Dusseldorf 
Dortmund 
Essen Request a free demo
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Philipp Thomaschewski
FRATCH CEO
