Azure Machine Learning Experts in Munich
matched fast from over 15,000 CVsHire experts who train, deploy and monitor machine learning models with Azure Machine Learning, MLflow and Azure Kubernetes Service. Get precise matches with vetted, available freelancers for your project.
Meet FRATCH Experts in Munich, who have recently used Azure Machine Learning
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.
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).
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.
Discover over 15,000 top freelancers
Statistics of experts using Azure Machine Learning
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
2.2 years
Positions per freelancer
10
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Manufacturing, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
83%
Doctorate
33%
Certifications per freelancer
6
Most common languages
German, English, Spanish
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 Azure Machine Learning
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.
Azure Machine Learning 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%)
- Manufacturing (71%)
- Professional Services (71%)
- Banking and Finance (57%)
- Retail (57%)
- Insurance (43%)
- Agriculture (29%)
- Chemical (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Azure Machine Learning does
Azure Machine Learning is a managed cloud service for building, training, deploying and governing machine learning models. It brings data preparation, experiment tracking, model registries, endpoints and monitoring into one Azure environment. Teams use it for forecasting, classification, recommendation, computer vision and natural language workloads.
Core project work
Experts use Azure Machine Learning to move models from experiments into controlled production workflows. Typical deliverables include:
- Reproducible training pipelines for batch and real-time inference
- Managed online endpoints and batch endpoints
- Feature preparation, model evaluation and versioned registries
- Monitoring for drift, data quality and inference performance
Ecosystem and tooling
The service supports Python-based frameworks such as scikit-learn, PyTorch, TensorFlow and XGBoost. Strong specialists also work with Azure Blob Storage, Azure Data Lake Storage, Azure Container Registry, Azure Kubernetes Service and Azure DevOps. MLflow integration helps teams track runs and manage model versions across the lifecycle.
When freelance expertise helps
Companies often bring in freelance specialists when a proof of concept must become a reliable service, when internal teams need Azure ML governance, or when existing models require a clearer path to production. They can also help connect data platforms, establish CI/CD for machine learning and reduce operational gaps. In Munich, remote collaboration is common, while regulated or workshop-heavy projects may require on-site coordination and strong German or English communication.
Signs you need a specialist
A project usually benefits from focused expertise when:
- Training runs cannot be reproduced across environments
- Models are deployed manually without approval or rollback controls
- Data drift and model quality are not monitored after release
- Costs, permissions or workspace structure are difficult to manage
- Data scientists and software teams lack a shared delivery process
What distinguishes strong professionals
The best professionals combine applied machine learning with practical Azure delivery skills. They understand data leakage, validation design, model explainability and responsible AI, but also know how to secure workspaces, automate pipelines and operate endpoints. During selection, ask for a clear example of a model moved into production, the controls used around it and how its performance was maintained over time.
Frequently asked questions
Curious about Azure Machine Learning? Here are the answers that come up again and again.
Azure Machine Learning is used to prepare data, train and evaluate models, register approved versions, and deploy them for batch or real-time inference. It supports use cases such as demand forecasting, fraud detection, predictive maintenance, recommendation and document analysis.
Azure Machine Learning is a strong choice for organizations already using Microsoft Azure, especially when identity, data services and deployment controls need to fit one cloud environment. Databricks often centers on collaborative data and analytics workflows, while Amazon SageMaker fits teams operating primarily on AWS; the right option depends on existing architecture, skills and governance needs.
A strong Azure Machine Learning specialist usually brings Python, SQL, data engineering and software delivery skills. Experience with MLflow, Docker, Azure DevOps, infrastructure as code, Azure Kubernetes Service and responsible AI is valuable when models must run reliably in production.
The required depth depends on the work. A focused model experiment may need applied modeling and workspace knowledge, while a production rollout calls for experience with pipelines, security, monitoring, deployment patterns and operational handover. Ask candidates to show work similar to the intended data, risk level and delivery stage.
Yes, most Azure Machine Learning work can be delivered remotely through cloud workspaces, version control and structured reviews. On-site sessions can still help with domain discovery, access setup or stakeholder workshops, and teams should agree early on whether German, English or both are needed.
Ask how the specialist handles reproducibility, data validation, model versioning, deployment approval and monitoring. A capable Azure Machine Learning professional can explain trade-offs clearly, provide practical testing evidence and connect technical choices to business and operational risks.
Azure Machine Learning can manage models created with common frameworks such as PyTorch, TensorFlow, scikit-learn and XGBoost. It can also use MLflow-based workflows, containerized environments and registered assets, allowing teams to bring existing experiments into Azure without rebuilding every component.
Freelancers should clarify the Azure subscription structure, workspace permissions, data access, compute limits, deployment target and acceptance criteria before starting. Experience with Azure Machine Learning is most useful when paired with careful documentation, secure handling of data and a delivery plan that covers the model after release.
The average hourly rate of freelancers in Munich, Germany who have used Azure Machine Learning in their recent projects is 118 €, which corresponds to a daily rate of about 947 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Azure Machine Learning in their recent projects, 100% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Munich, Germany who have used Azure Machine Learning in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Munich, Germany who have used Azure Machine Learning in their recent projects are German (100%), English (100%), and Spanish (29%).
The most common industries among freelancers in Munich, Germany who have used Azure Machine Learning in their recent projects are Information Technology (100%), Manufacturing (71%), and Professional Services (71%).
The most common business areas among freelancers in Munich, Germany who have used Azure Machine Learning in their recent projects are Information Technology (100%), Business Intelligence (86%), and Product Development (86%).
Main locations of FRATCH Experts, who have recently used Azure Machine Learning
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.
Countries:
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
