Amazon EKS Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Amazon EKS
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).
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
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...
Stephan Sahm
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)
Alexandru Gunescu
Last position:
Head of Cloud Infrastructure at BP
- Migrated the Electric Vehicle Charging SaaS App of the EV Division from on-premises and Azure to AWS Cloud, resulting in a hybrid multi-cloud multi-tenant solution
- Developed a streaming data pipeline using AWS MSK for Apache Kafka and implemented an event-driven architecture to ingest and process near real-time data from OCPI-protocol IoT devices
- Implemented multi-tenant strategies including database schema isolation, bridge model for resource sharing, and tenant-based RBAC controls
- Provisioned Kubernetes clusters on AWS EKS with namespaces and RBAC for tenant isolation
- Led migration from on-premises and Azure to AWS using AWS DataSync, Snowball, and Database Migration Service
- Orchestrated collaboration across 5+ systems, vendors, service providers, and on-site teams
- Supported development and maintenance of IT strategy aligned with business requirements
- Managed €40 million infrastructure budget with AWS & Azure cost optimization, achieving 15% savings
- Led 50+ developers to implement advanced database procedures, increasing productivity by 20%
- Spearheaded multi-cloud, multi-tenant infrastructure migration for 30% faster processing times
- Negotiated vendor pricing to reduce payroll/benefits administration costs by 20%
- Developed a two-year infrastructure technology roadmap yielding 25% cost savings
- Tech stack: Kubernetes on AWS EKS, Docker, Kafka/AWS MSK, Terraform, AWS CDK, TypeScript, React, NextJS, Node.js, NestJS, Python, Aurora Serverless, RDS (MySQL, SQL Server), GitHub Actions, Azure DevOps, ArgoCD, AWS Lambda, API Gateway, AWS Security Hub, AWS Database Migration Service, AWS DataSync, AWS Organizations, AWS Control Tower, Odoo, Microsoft Navision, MS Dynamics
Max Ritter
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)
Frank Eppink
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
Janusz Mazurek
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
21 years (Germany: 19 years)
Position duration
1.9 years (Germany: 2 years)
Positions per freelancer
12
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: 89%)
Master's degree or higher
63% (Germany: 45%)
Certifications per freelancer
4 (Germany: 3)
Most common languages
German, English, Spanish
Speak two or more languages
100% (Germany: 96%)
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 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
EKS on AWS
Amazon EKS, also known as Amazon Elastic Kubernetes Service or AWS EKS, runs Kubernetes without the overhead of managing the control plane yourself. It is used for containerized applications that need consistent deployment, scaling, and rollback on AWS. Companies bring it in when they want Kubernetes with tighter integration to IAM, VPC, and cloud-native operations.
What specialists deliver
- Cluster setup and upgrade planning
- Workload deployment with Helm or manifests
- Networking, ingress, and service exposure
- Access control with IAM and Kubernetes RBAC
- Logging, metrics, and alerting
Strong professionals know how to connect EKS with the rest of the AWS stack and keep it stable in day-to-day use.
When to bring help
Teams often need freelance expertise when a cluster is being introduced, migrated, or cleaned up after quick growth. That includes platform design, release automation, cost control, and security hardening. In Munich, this is common for software teams that work with regulated industries, connected products, or internal enterprise systems.
Typical project work
- Greenfield EKS environments
- Migration from self-managed Kubernetes
- CI/CD pipelines for container releases
- Multi-account and multi-environment setups
- Disaster recovery and backup strategy
Good experts leave clear runbooks, sensible defaults, and a setup the team can operate after handover.
Skills that matter
Amazon EKS work is not just Kubernetes knowledge. It also needs AWS networking, IAM, container image handling, secrets management, and troubleshooting across nodes, pods, and services. The best specialists understand how app teams, security teams, and operations teams use the cluster differently.
What strong experts do well
They keep deployments predictable, reduce cluster drift, and make troubleshooting simple. They know when to use managed add-ons, how to shape autoscaling, and how to avoid brittle configurations. For remote work or on-site sessions in Munich, strong communication matters as much as technical depth.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Amazon EKS.
Amazon EKS is used to run Kubernetes workloads on AWS with less infrastructure management. Companies use it for APIs, internal services, batch jobs, and container platforms that need standard Kubernetes tooling. It fits teams that want cloud integration without giving up Kubernetes control.
EKS is AWS’s managed Kubernetes service, not a different orchestrator. You still work with Kubernetes concepts such as pods, deployments, services, and namespaces, but AWS handles much of the control plane operation. The main difference is the AWS integration and the managed service model.
Amazon EKS help is useful when you are starting a cluster, migrating from another setup, or fixing problems that span networking, permissions, and deployment automation. Many companies also bring in specialists for security reviews, scaling issues, and platform cleanup. A freelancer can focus on the hard parts without long onboarding.
A strong Amazon EKS specialist usually knows Kubernetes manifests, Helm, IAM, VPC networking, container registries, and CI/CD pipelines. They should also understand observability, secrets handling, and how worker nodes are managed in AWS. If your setup uses GitOps or service mesh tools, that is a plus.
Amazon EKS removes a lot of control plane work compared with self-managed Kubernetes. That usually means less operational overhead and easier AWS integration, but you still need solid cluster design and workload management. It is a good choice when you want Kubernetes without running every layer yourself.
Most EKS work can be done remotely because it centers on cluster configuration, deployment pipelines, and troubleshooting. On-site time in Munich can help when teams need workshop-style planning, security alignment, or close work with multiple stakeholders. The best setup often combines remote delivery with a few focused local sessions.
Look for clear decisions, not just cluster access. A strong Amazon EKS freelancer can explain why a setup uses certain node groups, networking rules, scaling settings, and deployment patterns. Good signs are clean handover notes, practical monitoring, and a setup your team can maintain.
A good Amazon Elastic Kubernetes Service freelancer should ask about workload types, current AWS accounts, networking, security constraints, release flow, and who will operate the cluster after handover. They may also ask how much is already in Kubernetes and how much is still on EC2 or other platforms. Those answers shape the right implementation approach.
The average hourly rate of freelancers in Munich, Germany who have used Amazon EKS in their recent projects is 105 €, which corresponds to a daily rate of about 844 € 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 63% hold at least a Master's degree.
On average, freelancers in Munich, Germany who have used Amazon EKS in their recent projects have 21 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 (22%).
The most common industries among freelancers in Munich, Germany who have used Amazon EKS in their recent projects are Information Technology (100%), Automotive (67%), and Retail (67%).
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 (89%).
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.
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We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
