AWS CDK Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used AWS CDK
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).
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...
Alexandre Savio
Last position:
Cloud Engineer at Dectris AG
- Build a scalable multi-region backend service in AWS to serve remote desktop virtual machines for scientific analysis
- Stack: AWS, GitHub, Terraform, Python, Rust
- Built and defined the core infrastructure of the backend system
- Defined and coded the virtual machines provisioning supporting Ubuntu and Rocky Linux desktop setups
- Programmed the API service running in ECS to manage virtual machines and build custom Docker images for users
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)
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))
Stephan Menzel
Last position:
SAP
- Consulting and development for VR usage scenarios in industrial contexts
- Digital Twin, Unreal Engine VR deployments, Multi-user networking, Cloud infrastructure
- Technologies: AWS, Google Cloud, other Cloud Services; C++; Unreal Engine 5; Android, Meta Quest
Discover over 15,000 top freelancers
Statistics of experts using AWS CDK
Aggregated from the professional profiles of matched freelancers.
Experience
19 years (Germany: 16 years)
Position duration
2 years (Germany: 1.9 years)
Positions per freelancer
13 (Germany: 12)
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Automotive, Manufacturing
Certification focus areas
Information Technology, Business Intelligence, Operations
Bachelor's degree or higher
100% (Germany: 80%)
Master's degree or higher
83% (Germany: 47%)
Doctorate
17% (Germany: 10%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
German, English, Spanish
Speak two or more languages
100%
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 AWS CDK
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
AWS CDK turns cloud infrastructure into application code. Teams use it to define stacks, synthesize CloudFormation, and keep AWS resources in version control. It fits delivery work where repeatable environments matter more than manual setup.
What it builds
- Network and security foundations
- Serverless services and event flows
- Data layers, queues, and messaging
- Deployment pipelines and environment setup
CDK is a strong fit when projects need repeatable cloud delivery instead of one-off console changes.
The stack around it
AWS CDK specialists often work with CloudFormation, IAM, Lambda, API Gateway, S3, ECS, and Step Functions. They also use CDK constructs, context values, and asset handling to keep stacks clear and reusable. Good work here depends on a clean codebase and a solid grasp of AWS service behavior.
When teams bring in experts
Companies look for AWS CDK expertise when a cloud setup grows messy, deployment steps vary by environment, or new services must be added without breaking the existing stack. In Munich, this often comes up in product teams, platform work, and regulated environments that need careful change control.
What strong specialists do
- Keep stacks modular and easy to review
- Use secure defaults for IAM and networking
- Separate shared constructs from app-specific code
- Make deployment and rollback behavior predictable
Strong professionals know when to use high-level constructs and when to drop down to lower-level resources for control.
Working style and delivery
AWS CDK work is usually code-heavy and collaborative. The best experts can review existing IaC, fix drift between environments, and help teams move from raw CloudFormation or Terraform to CDK when the codebase and AWS setup justify it. Remote work is common, but on-site sessions in Munich can help align platform standards and release flows.
Frequently asked questions
Everything clients usually want to know about AWS CDK, in one place.
AWS CDK is used to define cloud infrastructure in code and deploy it to AWS through CloudFormation. Teams use it for stacks, networking, serverless backends, data services, and delivery pipelines. It is a good fit when infrastructure needs to be reusable, reviewed, and versioned like application code.
AWS CDK gives teams a programming model, so they can express infrastructure with familiar languages and reusable constructs. Compared with Terraform, it is often a better fit when AWS is the main target and the team wants more code reuse. Compared with raw CloudFormation, it usually feels faster to maintain, because you write higher-level code instead of large template files.
A strong AWS CDK specialist usually works in TypeScript, Python, Java, or C#. They should also know core AWS services such as IAM, Lambda, API Gateway, S3, ECS, Step Functions, and CloudFormation. The best people understand how those services behave in production, not just how to declare them in code.
Simple stacks can be handled by a general cloud specialist, but production CDK work benefits from someone who has shipped real AWS environments. You want a person who understands reusable constructs, environment separation, deployment safety, and security controls. If the stack is already in place, the right expert can also improve structure without forcing a full rewrite.
Most AWS CDK work can be done remotely because it is code, review, and deployment driven. On-site time in Munich can help when teams need to align on platform patterns, release practices, or security rules. Many companies use a mix of both, especially when infrastructure changes affect several product teams.
Look for clear stack design, secure IAM choices, and clean separation between reusable constructs and app-specific code. A good AWS CDK freelancer can explain trade-offs, show how they handle testing and deployment, and avoid hidden complexity. You should also ask how they manage imports, environment config, and rollback behavior.
Most AWS CDK professionals also need solid skills in CloudFormation, IAM, CI/CD, and AWS networking. Depending on the project, they may also need Docker, Kubernetes, observability tools, or service architecture knowledge. If the work touches security or compliance, experience with policy design is especially useful.
Bring in an AWS CDK expert when the setup is moving fast, the codebase has grown inconsistent, or deployment failures are slowing delivery. It also helps when you need a migration from manual setup or another IaC tool, or when a new team must inherit the platform cleanly. A specialist can stabilize the foundation and leave the team with patterns they can keep using.
The average hourly rate of freelancers in Munich, Germany who have used AWS CDK in their recent projects is 108 €, which corresponds to a daily rate of about 865 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used AWS CDK in their recent projects, 100% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Munich, Germany who have used AWS CDK 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 AWS CDK in their recent projects are German (100%), English (100%), and Spanish (38%).
The most common industries among freelancers in Munich, Germany who have used AWS CDK in their recent projects are Information Technology (100%), Automotive (75%), and Manufacturing (75%).
The most common business areas among freelancers in Munich, Germany who have used AWS CDK in their recent projects are Information Technology (100%), Product Development (100%), and Project Management (88%).
Main locations of FRATCH Experts, who have recently used AWS CDK
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 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Berlin Request a free demo
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Philipp Thomaschewski
FRATCH CEO
