Azure Machine Learning Experts in Munich
in minutes from vetted, available freelancers with the power of AI.Hire experts who build Azure ML workspaces, training pipelines, model deployment flows, and MLOps automation for teams in Munich and remote setups, with fast, precise matching to vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used Azure Machine Learning
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.
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).
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 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 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.
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 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 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Azure ML in practice
Azure Machine Learning is Microsoft’s cloud service for building, training, deploying, and monitoring machine learning models. Companies use it to move work from notebooks into repeatable pipelines, governed workspaces, and production endpoints. It is often shortened to Azure ML or AML, and many teams still say Azure ML Studio.
Typical delivery
- Workspace setup and access design
- Training jobs, environments, and compute targets
- Model registry, deployment, and endpoint management
- Monitoring, drift checks, and retraining flows
Strong professionals connect data prep, experimentation, and release steps so teams can ship models without ad hoc scripts. They know where Azure Machine Learning fits in the Microsoft stack and where it should stay close to other Azure services.
Ecosystem skills
Azure ML work usually touches Python, MLflow, Docker, Git, and Azure services such as Storage, Key Vault, and Azure Container Registry. Depending on the setup, specialists also work with Databricks, Synapse, or Kubernetes-backed deployment. Clean code, repeatable environments, and clear handover matter as much as model quality.
When to bring help
Companies often need outside experts when a proof of concept must become a stable platform, when deployments fail in real use, or when teams need clearer MLOps practices. In Munich, that can mean work with local product teams, industrial firms, or data groups that prefer a blend of on-site workshops and remote delivery. Azure ML specialists also help when internal staff know the data but need production experience.
What strong experts do
A good Azure Machine Learning specialist understands the full path from experiment to endpoint. They design pipelines that are easy to rerun, choose the right compute for the task, and keep environments controlled across dev and production. They also document decisions so operations teams can support the system after handover.
Signs of fit
- Clear use of managed compute and reusable environments
- Practical MLOps habits, not just notebook work
- Familiarity with Azure security and access control
- Ability to explain trade-offs between Azure ML and custom cloud setups
The best specialists write code that another expert can pick up quickly. They avoid lock-in where it hurts, but they also use Azure ML services where they save time and reduce risk.
Frequently asked questions
Curious about Azure Machine Learning? Here are the answers that come up again and again.
Azure Machine Learning is used to train, deploy, and monitor models in a managed Azure environment. Teams use it for forecasting, classification, anomaly detection, and other production ML workflows. It is a good fit when you need repeatable pipelines, controlled access, and clear model operations.
Azure Machine Learning is the current product name, and AML is a common shorthand. Azure ML Studio is an older name many people still use when they refer to the web interface and workspace experience. In hiring, it helps to search for all three because candidates may describe the same skill set differently.
Azure Machine Learning is strongest when the team wants a Microsoft-native path for model lifecycle management, deployment, and governance. Databricks is often chosen for broader data engineering and collaborative analytics, while SageMaker is the closest AWS alternative. The right choice depends on your cloud stack, operating model, and how much of the workflow must stay in Azure.
A strong Azure Machine Learning specialist usually knows Python, MLflow, Git, Docker, and basic Azure security. Many also work comfortably with data prep, CI/CD, and deployment patterns for APIs or batch jobs. If the project touches larger data flows, knowledge of Azure Storage, Key Vault, and Container Registry is helpful too.
With Azure Machine Learning, the need for outside help usually appears once a notebook prototype must become a stable service. If your team needs deployment, monitoring, environment management, or governance, a specialist can save a lot of trial and error. Small experiments can stay internal, but production work benefits from someone who has done the full lifecycle before.
Yes, many Azure Machine Learning projects work well remotely because the service is cloud-based and the key tasks are in code and configuration. In Munich, some teams still prefer on-site sessions for discovery, access planning, or handover with local stakeholders. A good specialist can usually combine both formats without slowing delivery.
Look for someone who talks about pipelines, environments, deployment, monitoring, and rollback, not only model accuracy. A strong Azure Machine Learning expert can explain how they managed data access, how they handled failures, and how they made the system maintainable. Good signs are clear documentation, clean repo structure, and practical decisions about Azure services.
A solid Azure Machine Learning engagement usually ends with a working workspace setup, reusable training jobs, deployment assets, and clear runbooks. Depending on scope, you may also get monitoring logic, retraining triggers, and handover notes for operations. The best deliverables are designed so another specialist can support them later without guessing.
The average hourly rate of freelancers in Munich, Germany who have used Azure Machine Learning in their recent projects is 117 €, which corresponds to a daily rate of about 938 € 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
