Amazon SageMaker Experts in Munich
matched in minutesHire experts who train, deploy and monitor machine learning models with Amazon SageMaker, connect data pipelines across AWS and operationalize intelligent products. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used Amazon SageMaker
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
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.
Stephan B.
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
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)
Maziyar K.
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 K.
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 Sprint™, 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.
Himanshu N.
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.
Michael O.
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
Martin M.
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 S.
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 W.
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 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 SageMaker
Aggregated from the professional profiles of matched freelancers.
Experience
16 years (Germany: 14 years)
Position duration
2 years (Germany: 1.9 years)
Positions per freelancer
11 (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: 68%)
Doctorate
23% (Germany: 20%)
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 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 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Amazon SageMaker 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%)
- Professional Services (85%)
- Banking and Finance (69%)
- Manufacturing (46%)
- Media and Entertainment (46%)
- Automotive (38%)
- Education (38%)
- Insurance (38%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Amazon SageMaker does
Amazon SageMaker is an AWS service for building, training, deploying and operating machine learning models. It brings notebooks, data preparation, model development, hosting and monitoring into a managed environment. Teams use it to turn experiments into repeatable production services without managing every underlying server.
Where it is used
Amazon SageMaker supports prediction services, recommendation engines, demand forecasting, fraud detection, document processing and image or language applications. It can serve models in real time, run batch inference or support large-scale training workflows. Typical projects include:
- Deploying managed endpoints for product or customer predictions
- Creating forecasting and classification pipelines
- Automating model training and release workflows
- Monitoring drift, latency and prediction quality
AWS ecosystem and tooling
Strong work with SageMaker connects the service to Amazon S3, AWS Glue, Amazon Redshift, Amazon EMR and Amazon ECR. Specialists may also use SageMaker Studio, Processing Jobs, Training Jobs, Pipelines, Feature Store and Model Registry. Secure delivery commonly involves IAM, VPC networking, CloudWatch, Step Functions and infrastructure as code.
When companies need expertise
Companies often bring in freelance specialists when a proof of concept must become a dependable service, when training costs or deployment risks need control, or when an existing AWS machine learning setup has become difficult to operate. In Munich, collaboration may combine remote delivery with on-site workshops for teams in manufacturing, mobility, finance, healthcare or retail.
- A model works in a notebook but lacks a production path
- Training and inference workflows are manual or hard to reproduce
- Data, security and model operations are split across teams
- Monitoring does not reveal drift or service failures
Skills that matter
Effective SageMaker professionals understand both machine learning workflows and cloud operations. They work with Python, SQL, containers, APIs, distributed training and data quality controls. They also know how to design IAM permissions, private networking, reproducible pipelines and clear handovers for teams with different language and delivery needs.
How quality is judged
A strong specialist can explain why SageMaker is the right fit instead of forcing every workload into it. Look for evidence of reproducible training, sensible model evaluation, secure deployment, cost-aware architecture and useful monitoring. The best deliverables include documented pipelines, tested infrastructure, operational runbooks and a clear process for retraining or retiring models.
Frequently asked questions
Curious about Amazon SageMaker? Here are the answers that come up again and again.
Amazon SageMaker is used to prepare data, train machine learning models, deploy predictions and monitor models in production. It supports real-time endpoints, batch inference and automated workflows within AWS.
AWS SageMaker provides managed tools for training, hosting, pipelines and monitoring, while EC2 offers more direct control over servers and software. SageMaker can reduce operational work, but EC2 may suit workloads that need unusual configurations or very specific infrastructure control.
A strong Amazon SageMaker specialist should understand AWS IAM, S3, VPC networking, CloudWatch, containers and infrastructure as code. Python, SQL, data engineering, model evaluation and CI/CD practices are also useful for connecting experiments with reliable production systems.
The right level depends on the scope, not on a fixed number of years. A focused endpoint or pipeline may need a specialist familiar with the relevant AWS services, while a regulated production platform calls for experience with security, reproducibility, monitoring and operational ownership.
Amazon SageMaker is well suited to remote collaboration because code, infrastructure and workflows can be reviewed through shared repositories and AWS environments. For teams in Munich, remote delivery can be combined with on-site workshops, with clear documentation and German or English communication agreed in advance.
Ask how the specialist would move from data preparation to training, deployment and monitoring. A capable SageMaker expert should discuss IAM boundaries, reproducible pipelines, failure handling, model drift, testing and rollback rather than focusing only on notebook experiments.
Amazon SageMaker fits many managed training and inference workflows, but it is not automatically the best choice for every case. Teams may prefer specialised AWS services, Kubernetes-based tooling, direct EC2 control or another cloud when portability, custom infrastructure or a different operating model matters more.
A SageMaker freelancer should leave behind reproducible code, documented data and model workflows, secure infrastructure configuration and deployment instructions. Useful handover material also covers monitoring, incident response, retraining, access permissions and the boundaries of ongoing ownership.
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 898 € 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 (85%), 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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Philipp Thomaschewski
FRATCH CEO
