Amazon SageMaker Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Amazon SageMaker
Michael Nelz
Last position:
Senior ML Engineer, AI Engineer at Lanxess AG
- Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
- Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
- Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.
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
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.
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)
Stephan Baier
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Maziyar Khorrami
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
Biju Krishnan
Last position:
Freelance AI Strategist & Governance Expert at DataSiens Freelancer
- Developed the AI strategy for a major Austrian retailer with over €10 billion in annual revenue.
- Developed a go-to-market strategy for AI services for a Norwegian consulting firm specializing in SAP technologies.
- Delivered AI for Business training programs to a leading German supermarket chain.
- Defined AI governance project structure and roadmap for a large German manufacturer.
- Certified facilitator for AI Design Sprintâ„¢, leading use case discovery workshops for large enterprises.
- IEEE Certified AI Ethics Assessor with expertise in building AI governance frameworks aligned with the EU AI Act.
- Founder of aiethicsassessor.com as knowledge base for AI governance and AI legislation.
- Author of a best-selling Udemy course on Data Architecture.
- Developed intelligent agents using low-code/no-code platforms to automate complex business processes.
Michael Odenthal
Last position:
Co-Founder & Managing Director at querifai GmbH
- Establishing platform querifai.ai to help non-programmers to easily test and compare third party AI services
- Building custom AI-based workflows for clients on querifai
- Conducting projects at the intersection of management consulting and data science
Himanshu Negi
Last position:
Principal (Data Scientist/Data Engineer/Gen AI Engineer) at Marktguru Deutschland GmbH
Architected an agentic, real-time offer orchestration engine where specialized agents (retrieval, pricing/optimization, and policy/guardrails) coordinate to personalise promotions across customer touchpoints using RAG with FAISS over Delta Lake and low-latency Databricks Model Serving. Collaborated with product managers and commercial stakeholders to shape the roadmap and evaluate emerging agent patterns for production.
Designed an agent-based data quality service that orchestrates schema detection, entity normalization, and validator/exception-handling agents to clean multi-retailer SKU feeds at scale. Wrapped model calls in PySpark UDFs for distributed inference, automated via Databricks Workflows and CI/CD.
Developed a multimodal, agentic extraction pipeline where vision, parsing, and compliance agents collaborate to derive brand, packaging, and volume from scanned images using Claude 3 Sonnet with Swin Transformer encoders. Orchestrated via Azure Event Hub with outputs persisted to Delta Lake.
Implemented a GS1 taxonomy classification service built around cooperating agents for inference, drift monitoring, and auto-retraining governance using Falcon 180B (LoRA-tuned) with a batch pipeline on Databricks.
Created a hybrid agent workflow where a retrieval agent surfaces candidate matches via embeddings and a reasoning/verification agent (Mixtral 8x7B) adjudicates receipt-to-SKU alignment, integrated into a streaming Databricks pipeline.
Built a multimodal attribute inference pipeline structured as cooperating vision-language, rules/consistency, and compliance agents to fill NutriScore, nutrition fields, and packaging types from names and images using LLaMA 3-8B with CLIP embeddings.
Developed a GenAI-powered orchestration system that ingests recipes from multiple websites, parses ingredients through structured extraction agents, and dynamically links them to real-time retailer offers via tagging, semantic reasoning, and business-rule agents.
Martin Musiol
Last position:
Product Owner AI Learning Platform at B2B Tech Scale-Up
- Agile setup of a multimodal analysis platform for training materials (video, audio, documents) using Scrum
- Extraction of context-relevant content based on user profiles & competency dimensions
- Personalized delivery of learning content to boost sales performance
- Close coordination with sales teams & stakeholders to validate features
- Use of Gemini, Whisper, Python & JavaScript, deployment on AWS, Perl for scripting data imports
- Integration into existing tools & CRM systems for smooth adoption
- Technologies used: Python, OpenAI, DB tech like PostgreSQL, CI/CD for Airflow DAGs, FastAPI
Mohamed Saleh
Last position:
Machine Learning Engineer (Part Time) at E.ON Digital Technology
- Designed and implemented an advanced, agentic RAG pipeline using LangChain and LangGraph for structured data extraction from PDFs, utilizing tools, state management, and OpenAI LLMs (GPT-4) to improve accuracy and handle complex document structures.
- Developed a Google AI agent for extraction of structured information from PDF documents and deployed the agent on Vertex AI.
- Architected data pipelines using Azure Data Factory and Databricks to ingest data from Azure Blob Storage, process it with PySpark, and load it into Azure SQL Database via Linked Services.
- Containerized AI agents and services using Docker for consistent local development and deployment.
- Utilized PySpark and Dask for database querying in coordination with Azure Blob Storage and Document Storage.
- Created a ReAct agent that extracts structured data from PDF documents using tools and integrating Azure Document Intelligence.
- Contributed to the CPO invoices validation check project using Databricks to find existing CDRs and calculate total valid costs.
- Developed a conversational AI agent (chatbot) with a FastAPI backend, integrating RAG for precise tariff extraction and deployed the service using Azure Container Apps.
- Tools used: Azure, Azure OpenAI, Azure Document Intelligence, Azure Blob Storage, Google ADK, Google Cloud, Vertex AI, Gemini, Databricks, LangChain, LlamaIndex Ollama, Docker, PySpark, Azure SQL, Azure Data Factory, Azure AI Agent, Microsoft SQL Server
Jan Wahler
Last position:
Technical Consultant at AI Beratung (KMU)
- Evaluation of RAG for legal advisory (build or buy)
- Evaluation and POC of RAG for an ERP time tracking module
- Consulting on foundation model selection
- Setup AI development environment (eliminating shadow AI)
- AI strategy consulting
- AI-assisted code creation and context engineering make change sets larger
- Strong software engineering expertise, code reviews and safeguarding through pipelines and domain-specific automated test cases
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 SageMaker
Aggregated from the professional profiles of matched freelancers.
Experience
16 years (Germany: 14 years)
Position duration
2 years
Positions per freelancer
10 (Germany: 9)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Professional Services, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
85% (Germany: 69%)
Doctorate
23% (Germany: 19%)
Certifications per freelancer
6 (Germany: 3)
Most common languages
German, English, Italian
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 Amazon SageMaker
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
What it is
Amazon SageMaker is AWS’s managed machine learning service for building, training, and deploying models without running the full infrastructure stack yourself. Teams use it to move from data prep to model hosting with one environment and clear operational controls.
Typical work
- Prepare notebooks, training jobs, and model artifacts
- Set up endpoints for real-time or batch inference
- Orchestrate data flows with the wider AWS stack
- Tune models and monitor performance in production
Where it fits
SageMaker is common in products that need prediction, ranking, forecasting, or document analysis. In Munich, it often shows up in automotive, industrial, finance, and B2B software projects where AWS is already the base platform and teams want repeatable ML delivery.
What strong specialists do
Strong professionals know how to work with IAM, S3, ECR, CloudWatch, and networking around the service, not just the model code. They choose the right training and deployment pattern, keep costs under control, and make the setup understandable for the rest of the team.
When companies bring in help
Companies usually look for freelance expertise when a SageMaker setup must be stabilized, extended, or handed over. That can mean moving from experiments to production, fixing pipeline issues, improving endpoint reliability, or adapting an existing AWS ML workflow after a change in scope.
What to look for
Look for experts who can explain trade-offs clearly and deliver working assets, not vague ideas.
- Practical AWS and MLOps knowledge
- Experience with notebooks, pipelines, and deployment
- Clear habits for testing, monitoring, and handover
- Good communication for remote or on-site work in Munich
Frequently asked questions
Curious about Amazon SageMaker? Here are the answers that come up again and again.
Amazon SageMaker is used to build, train, deploy, and monitor machine learning models on AWS. Companies use it for predictions, recommendations, forecasting, anomaly detection, and document or image workflows. It helps teams keep the ML lifecycle in one place instead of stitching together many separate tools.
SageMaker gives you a managed path for training jobs, hosted endpoints, pipelines, and model monitoring. Compared with assembling everything from EC2, S3, and custom scripts, it reduces platform work and speeds up delivery. The trade-off is that you still need strong AWS judgment to keep the setup clean and cost-aware.
Bring in an Amazon SageMaker specialist when experiments need to become a stable production setup. Common reasons are pipeline failures, slow training runs, endpoint issues, missing monitoring, or a handover from data science into a real AWS environment. It also helps when an existing team knows ML but not the service details.
A good freelancer usually combines SageMaker with AWS basics such as IAM, S3, ECR, VPC networking, and CloudWatch. Python, container work, model packaging, and MLOps practices are also important. For some projects, knowledge of data engineering and CI/CD matters just as much as model code.
Yes. Amazon SageMaker work is often done remotely because the core tasks live in AWS and can be reviewed through code, pipelines, and cloud logs. For projects in Munich, on-site sessions can still help at the start, especially when teams need workshops, access alignment, or a smooth transfer to internal specialists.
It depends on the scope, but most projects need more than basic notebook knowledge. A SageMaker freelancer should have shipped at least one real workflow that covers training, deployment, and monitoring, not just prototypes. If the project includes security, scaling, or governance, deeper AWS experience becomes important.
Ask for concrete examples of shipped work and for a clear explanation of design choices. A strong Amazon SageMaker professional can describe how they handled data input, model versioning, deployment, rollback, and observability. Good answers are specific, practical, and tied to production outcomes.
In the first week, an AWS SageMaker expert should review the current setup, identify risks, and map the path from data to deployment. They should also clarify what can be reused, what must be rebuilt, and which AWS services are part of the solution. If the plan stays vague, that is a warning sign.
The average hourly rate of freelancers in Munich, Germany who have used Amazon SageMaker in their recent projects is 112 €, which corresponds to a daily rate of about 894 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Amazon SageMaker in their recent projects, 100% hold at least a Bachelor's degree, 85% hold at least a Master's degree, and 23% hold a doctorate.
On average, freelancers in Munich, Germany who have used Amazon SageMaker in their recent projects have 16 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 Amazon SageMaker in their recent projects are German (100%), English (100%), and Italian (23%).
The most common industries among freelancers in Munich, Germany who have used Amazon SageMaker in their recent projects are Information Technology (100%), Professional Services (77%), and Banking and Finance (69%).
The most common business areas among freelancers in Munich, Germany who have used Amazon SageMaker in their recent projects are Information Technology (100%), Product Development (92%), and Business Intelligence (77%).
Main locations of FRATCH Experts, who have recently used Amazon SageMaker
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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We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
