Amazon EKS Experts in Munich
matched in minutes from over 15,000 CVsHire experts who run production Kubernetes clusters on Amazon EKS, automate delivery with infrastructure as code, and connect workloads to AWS networking, security and observability services. FRATCH matches you quickly with vetted, available freelancers who fit your project precisely.
Meet FRATCH Experts in Munich, who have recently used Amazon EKS
Alexandru G.
Last position:
Principal Cloud DevOps Architect at BP
In my role as Senior Cloud DevOps Architect for BP, an oil and gas company, I had the mission to migrate the Electric Vehicle Charging platform of the EV Division from on-premises and Azure to AWS cloud, resulting in a hybrid multi-cloud, multi-tenant SaaS solution.
Deployment with Kubernetes for the application layer meant provisioning Kubernetes clusters managed by EKS and AKS, with a focus on integrating them into a multi-tenant environment. This integration was achieved by using Kubernetes namespaces and access controls to ensure data isolation and privacy enforcement.
In the database layer, we chose an RDS instance with PostgreSQL to support the backend infrastructure of our applications. Tenants shared the same RDS instance, but each had a dedicated schema.
To ingest near real-time data from physical charge points (CPOs), as IoT devices, via the OCPI protocol, we ran into significant delays with batch processing. As a result, we built a real-time streaming data pipeline using Apache Kafka, while prioritizing an event-driven architecture.
Led collaboration across multiple internal teams, external vendors, cloud providers, and on-site partners to integrate over five systems into a unified solution.
Achievements:
- Successfully designed and implemented hybrid multi-cloud solutions, integrating multiple cloud platforms (AWS, Azure) with on-premises infrastructure, using Site-to-Site VPNs, Firewalls, and Load Balancing.
- Led the migration of on-premises infrastructure to multi-cloud, multi-tenant infrastructure, resulting in 30% faster processing times.
- Migrated workloads from VMware and Hyper-V environments to cloud-based VMs, leveraging cloud-native services to optimize performance, cost efficiency, and scalability.
- Designed a multi-tenant Kubernetes platform leveraging the Kubernetes ecosystem, using Karpenter for dynamic EC2 node provisioning, KEDA for event-driven pod autoscaling (e.g., Kafka message lag), and Rancher for centralized monitoring of multiple clusters (EKS, AKS, or on-prem K8s), replacing Microsoft-centric Azure Arc management service.
- Designed and implemented Python-based FastAPI microservices as part of the EV core-backend on AWS EKS application layer, powering data ingestion and customer analytics pipelines.
- Developed asynchronous, event-driven APIs (Python-FastAPI) for real-time integration with CPOs, supporting OCPI 2.3 and OICP protocols.
- Designed and implemented a secure, production-grade Azure Databricks platform using Terraform, ensuring scalability and cost efficiency.
- Migrated on-premises ERP to a hybrid Dynamics 365 architecture with ERP hosted locally and CRM running in Azure, integrated via Azure Arc.
- Automated CI/CD pipelines for Databricks notebooks and jobs using GitHub Actions & Databricks CLI, reducing deployment time. Reduced infrastructure provisioning time by 70% by automating cloud resource deployment with GitOps.
- Ensured compliance with internal audit and data governance standards (GDPR) through OAuth2/OIDC-based authentication and fine-grained role-based access controls.
- Developed a Zero Trust security model, enforcing least-privilege access and microsegmentation, enhancing security posture and compliance with GDPR and NIST.
- Built interactive analytics dashboards in Amazon QuickSight, integrating data from S3 and Redshift to deliver real-time business insights and visualizations with embedded access for multi-tenant users.
- Led cloud security assessments and full-lifecycle cybersecurity integration during M&A, covering AWS, Azure, IAM (Entra ID), and data protection, while aligning security posture with NIST, ISO 27001, and GDPR across hybrid and cloud-native environments.
- Reduced cloud costs by 64% for a client's dev environment by implementing automated start/stop schedules for EC2 and RDS instances via AWS CDK with EventBridge Scheduler or AWS Systems Manager.
Tech stack:
- Infrastructure as Code: Terraform, AWS CDK, Ansible.
- Containers: Kubernetes on EKS, AKS, Docker.
- Streaming Data Processing: Kafka to Confluent Cloud, after AWS MSK.
- Frontend: TypeScript, React, NextJS, Hooks, Styled Components.
- Backend: Python with FastAPI, also Node.js with NestJS.
- Database: Aurora on PostgreSQL with TypeORM, RDS on SQL Server, Azure Databricks full setup and administration, ETL Pipelines.
- CI/CD and GitOps: GitHub Actions, Azure DevOps, ArgoCD.
- Monitoring and Observability: Prometheus and Grafana.
- Virtualization: Hyper-V, VMware Cloud on AWS, Azure Migrate.
- ERP Systems: Odoo, Microsoft Dynamics 365 Business Central on Azure, integrated with Azure Arc.
- Networking: Site-to-Site VPNs, AWS Direct Connect, Azure ExpressRoute, Firewalls (AWS Network Firewall, Azure Firewall).
- Security: IAM, NIST Framework, Zero Trust Security, AWS WAF, AWS Shield, GuardDuty.
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).
Srinivasu K.
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
Frank E.
Last position:
DevOps at Lauck-IT
Operations and extensions of Azure DevOps pipelines
Operations and extensions of AWS services
Citrix (Windows 10, Bitwarden)
AWS: ECR, EKS, CloudFront CDN, Route 53, VPC peering and CNI upgrade, Atlas MongoDB, S3 buckets, static website hosting
Azure: build and deploy with DevOps pipelines
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.
Tobias N.
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...
Anton K.
Last position:
Head of Overall Technical Integration NSC / Hadoop Cloud Development at IABG
Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).
Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.
Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management lifecycle.
Kubernetes, OpenStack and Hadoop are used as the foundation.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.
Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.
Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, DB.
Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
Stephan S.
Last position:
Senior Data/ML Consultant & Technical Lead at Jolin.io
Role: Software Engineer & Applied Mathematician (Mathematical optimization for scheduling; duration: 1 months; team setting: Team of 2, remote; technologies: JuMP, Julia, Pluto, Svelte, JavaScript, TypeScript, JetBrains Space, Terraform, Nomad)
Role: Software & Cloud & Web Engineer (Building scalable data science compute cluster from scratch; duration: 11 months; team setting: Team of 1, on-site; technologies: Terraform, Kubernetes, k8s ingress, k8s services, k8s RBAC, k8s networking, k3s, etcd, S3, DNS, certificates, Julia, Pluto, JavaScript, Tailwind, Astro, npm, Parcel, Preact, MUI, JWT, AWS SQS, AWS RDS, Python, GitLab, GitHub)
Role: AI & Web Engineer (Custom ChatGPT service; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Poetry, LangChain, Tailwind, ChatGPT API, Flask, FastAPI)
Role: Architect & Data Engineer (Central datalake setup and ingestion; duration: 9 months; team setting: Team of 5, remote; technologies: Infrastructure-as-code, AWS CDK, Python, Boto3, PySpark, AWS Glue, IAM, S3, ECS, Fargate, Lambda, Apache Hudi, DeltaLake, Databricks, GitHub, Jira, Miro)
Role: Software Engineer (PoC Julia migration of scikit-decide; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Julia, GitHub)
Max R.
Last position:
Cloud (AWS) | AI | DevOps | Data at Boehringer Ingelheim
- Architected and implemented an enterprise-grade AI Agent Platform leveraging Retrieval Augmented Generation (RAG) architecture to enhance clinical data insights.
- Established robust CI/CD pipelines for LLM applications using CDK and Jenkins, significantly reducing deployment times.
- Implemented comprehensive observability solutions that increased agent reliability across pharmaceutical environments.
- Designed scalable AI workflows with advanced orchestration that optimized context handling for enterprise data sources.
- Technologies: AI Agents (LangChain, LangGraph, Bedrock, Smolagents, Streamlit); LLM Operations (Tracing, Testing, Evaluation, LangSmith, LangFuse); Infrastructure-As-Code (AWS CDK, Terraform, Typescript, Jenkins); Vectors, Embeddings, RAG (OpenSearch, pgvector, PDF Extraction)
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))
Discover over 15,000 top freelancers
Statistics of experts using Amazon EKS
Aggregated from the professional profiles of matched freelancers.
Experience
23 years (Germany: 19 years)
Position duration
1.9 years (Germany: 2 years)
Positions per freelancer
15 (Germany: 13)
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Automotive, Retail
Certification focus areas
Information Technology, Business Intelligence, Operations
Bachelor's degree or higher
100% (Germany: 87%)
Master's degree or higher
67% (Germany: 44%)
Certifications per freelancer
4 (Germany: 3)
Most common languages
German, English, Spanish
Speak two or more languages
100% (Germany: 95%)
Based on our profile pool as of 19 Sep 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 Amazon EKS
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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Amazon EKS 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 (70%)
- Retail (70%)
- Banking and Finance (50%)
- Manufacturing (50%)
- Energy (40%)
- Insurance (40%)
- Education (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Amazon EKS does
Amazon Elastic Kubernetes Service, commonly called Amazon EKS, is AWS’s managed Kubernetes service. It runs the Kubernetes control plane while teams manage workloads, cluster configuration and delivery processes. Companies use it to operate containerised applications across cloud environments with AWS services close at hand.
Where it fits
Amazon EKS supports APIs, web platforms, data services, event-driven workloads and internal applications that need repeatable deployment. It is common in regulated industries, software companies and Munich-based organisations modernising applications or consolidating infrastructure.
- Run containerised services across managed Kubernetes clusters
- Separate workloads with namespaces, node groups and policies
- Scale applications with Kubernetes and AWS capabilities
- Connect services to load balancing, storage and identity controls
Ecosystem and tooling
Strong work with EKS combines Kubernetes with AWS architecture. Relevant tooling includes Amazon VPC, IAM, Elastic Load Balancing, ECR, CloudWatch, Route 53 and managed databases. Specialists also use Helm, Kustomize, Argo CD or Flux, Terraform or AWS CloudFormation, and CI/CD systems to create consistent environments.
When expertise matters
Companies often bring in freelance specialists during a migration from virtual machines, a platform redesign or a production reliability initiative. Expertise is also useful when clusters have grown difficult to secure, observe or operate, or when application teams need a clear internal platform.
- Design cluster, network and workload boundaries
- Move services from existing infrastructure into containers
- Establish deployment, rollback and release controls
- Improve monitoring, logging, autoscaling and incident response
What strong specialists deliver
A capable professional treats EKS as more than a cluster setup. They document trade-offs, automate repeatable changes, define access and cost controls, and test failure scenarios. They understand Kubernetes scheduling, networking, storage, upgrades and workload security, while knowing when an AWS managed service is the simpler choice.
Working with a Munich specialist
EKS projects benefit from close coordination between application, security and infrastructure teams. A Munich-based expert can join on-site workshops when architecture decisions require them, while remote collaboration works well for reviews, automation and operational handover. Clear communication in English, and German where needed, should be agreed at the start.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Amazon EKS.
Amazon EKS is used to run and manage containerised applications with Kubernetes on AWS. It suits service-oriented applications, APIs, batch workloads and internal platforms that need controlled deployment, scaling and operations.
Amazon EKS manages the Kubernetes control plane and integrates closely with AWS identity, networking and observability services. A self-managed cluster can provide more direct control, but it also leaves more patching, availability and operational work with the company.
A strong Amazon EKS specialist should understand Kubernetes networking, container images, Linux, IAM and AWS VPC design. Infrastructure as code, Helm, GitOps, CI/CD, monitoring and security are also valuable for production work.
The right level depends on the scope, workload risk and existing platform maturity. Amazon EKS work for a production migration or security redesign calls for someone who has operated comparable Kubernetes environments, while a contained development cluster may need a narrower skill set.
Yes. Amazon EKS projects are well suited to remote design sessions, infrastructure reviews, pair work and documented handovers. Munich teams may still prefer occasional on-site workshops, especially for migration planning or coordination with security and application groups.
Ask the specialist to explain cluster boundaries, access controls, upgrade planning, observability and recovery scenarios. Good Amazon EKS work is reproducible through automation, clear about trade-offs and tested against realistic workload failures.
Amazon EKS may be excessive for a small, stable application that can run on a simpler managed service. A specialist should compare operational complexity, team skills and workload needs rather than recommending Kubernetes by default.
Freelancers working with Amazon EKS should be comfortable with AWS account structures, data protection discussions and collaboration across security and application teams. For a Munich engagement, clarify whether workshops require on-site attendance and whether project communication is expected in English, German or both.
The average hourly rate of freelancers in Munich, Germany who have used Amazon EKS in their recent projects is 102 €, which corresponds to a daily rate of about 818 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Amazon EKS in their recent projects, 100% hold at least a Bachelor's degree and 67% hold at least a Master's degree.
On average, freelancers in Munich, Germany who have used Amazon EKS in their recent projects have 23 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Munich, Germany who have used Amazon EKS in their recent projects are German (100%), English (100%), and Spanish (30%).
The most common industries among freelancers in Munich, Germany who have used Amazon EKS in their recent projects are Information Technology (100%), Automotive (70%), and Retail (70%).
The most common business areas among freelancers in Munich, Germany who have used Amazon EKS in their recent projects are Information Technology (100%), Product Development (100%), and Project Management (90%).
Main locations of FRATCH Experts, who have recently used Amazon EKS
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 
Frankfurt 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
