Azure Machine Learning Experts in Germany
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Meet FRATCH Experts in Germany, 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.
Niklas Witzel
Last position:
AI Engineer at Tensora GmbH
- Designed and developed a multi-tenant SaaS platform enabling organizations to build their own knowledge bases and chat with brand-customized AI assistants (white-label approach with dynamic branding per organization).
- Implemented a scalable RAG architecture with a GPT-4o tool-use loop, hybrid semantic search, and strict tenant isolation at database and search index level.
- Built persistent, project-like chat sessions including a streaming API (SSE), multilingual support, and speech input/output (STT/TTS).
- Delivered the cloud infrastructure as Infrastructure-as-Code, fully automated per-customer CI/CD pipelines, and an onboarding process for new tenants.
Technologies used: Python, FastAPI, Pydantic (v2 noted), Next.js, React, TypeScript, Tailwind CSS, OpenAI / LLMs (GPT-4o), Azure AI Search, Cosmos DB, Azure Blob Storage, Azure Cognitive Services Speech, Azure App Service, Azure Container Registry, Retrieval-Augmented Generation (RAG), Server-Sent Events (SSE), Docker, Terraform, GitHub Actions, REST, OpenID Connect (OIDC), Multi-Tenancy
Abhishek Nair
Last position:
Fullstack Developer at DAMALO GmbH
- Own full-stack development of an AI-native enterprise platform built on TypeScript, React, Vite, tRPC, Hono, and PostgreSQL, delivering AI-powered consulting workflows to B2B clients.
- Designed and shipped a multi-agent AI system using ReAct framework and Claude skills-style workflow patterns, including an intelligent PM assistant with rich system prompts, slash commands, tool integrations, and streaming chat UI.
- Architected an LLM evaluation framework: rubric-based LLM-as-judge, golden datasets, regression testing, and automated quality gating — ensuring consistent AI output quality at scale.
- Integrated LangFuse for end-to-end LLM tracing, conversation replays, and evaluation pipelines, enabling data-driven prompt optimisation that reduced token costs and response variance.
- Built with Drizzle ORM, pgvector, and knowledge graphs for structured data access, semantic search, and relationship-aware AI reasoning across the platform.
- Led TanStack React Query migration across the application — replacing manual state management with centralised caching and automatic refetching, reducing data-fetching boilerplate significantly.
- Practiced AI-native development throughout: Claude Code, Codex, Perplexity SDK, and LLM-assisted testing across the full development lifecycle. Deployed on Vercel + Azure ACA with Biome for linting/formatting.
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).
Afaq Afaq Saeed
Last position:
Master’s Thesis Researcher – Multiview Perception Evaluation at Volkswagen AG
- Developed an evaluation framework for AI-generated multiview driving videos intended for perception and embodied-AI/VLA-related training workflows.
- Designed automated checks for temporal coherence, cross-camera consistency, semantic correctness, and multiview geometric quality, exposing failure modes relevant to autonomous systems.
- Combined classical computer vision, learned visual representations, and vision-language models to convert complex video artifacts into measurable engineering signals.
- Built repeatable benchmarking and failure-analysis workflows to support model comparison, data-quality decisions, and system-improvement discussions.
Julia Sagert
Last position:
Senior Data Scientist / Consultant at Cloud Nation GmbH
Python, SQL, PySpark, Databricks, Databricks SQL, Delta Lake, dbt, Azure Data Lake Storage, Azure Machine Learning, Azure DevOps, Power BI, Git, MLflow
- Developed, validated, and optimized predictive analytics and classification models using Python (pandas), SQL, and modern ML frameworks.
- Performed data analysis, feature engineering, model validation, cross-validation, and stability analysis to ensure robust model quality and performance.
- Communicated model assumptions, results, uncertainties, and limitations to business units, management, and technical stakeholders.
- Built scalable data and machine learning workflows in cloud-based analytics environments using Databricks and Microsoft Azure.
Basem Elasioty
Last position:
Head of Cloud & AI at VxLabs GmbH
- Led cloud and data engineering organization, defining architecture strategy for next-generation data platforms
- Designed and delivered an automotive fleet data management system including scalable ingestion pipelines, signal catalog management, and campaign processing workflows
- Built cloud-native microservices and streaming architectures supporting real-time vehicle data and AI-powered threat detection
- Established engineering standards for data quality, security, lineage, and governance in alignment with ISO/SAE 21434 and GDPR
- Managed engineering teams across data, backend, cloud, and AI functions, ensuring consistent delivery of high-quality, production-ready solutions
Utsav Rabadiya
Last position:
Working Student Junior Data Scientist (Performance Team GT Fleet) at Uniper SE
- Analyzed large-scale power plant data to develop and optimize key performance indicators (KPIs) for fleet-wide performance monitoring.
- Designed and developed interactive Power BI dashboards to provide real-time insights into key business metrics, improving decision-making processes across departments.
- Collaborated with site engineers and asset management to harmonize performance metrics across multiple countries.
- Supported digital transformation initiatives by implementing data-driven use cases using agile project management methods.
- Utilized OSIsoft PI systems for time-series data analysis and visualization to improve operational insights.
Kiran Kumar Kanathala
Last position:
Applied NLP: Word-Level Encoding for Smarter Event Predictions at University of Siegen
- Engineered 12+ Seq2Seq models (LSTMs/GRUs) to train an AI model, supporting AI Agent Evaluation Analyst and online projects in complex systems.
- Conducted 15+ experiments to improve forecasting accuracy by 28%, applying analytical thinking and testing models for QA and edge case coverage.
- Researched 20+ papers as a consultant to guide Large Language Model design, ensuring logical implications and domain of expertise alignment.
- Saved 40% compute time via model compression with reusable PyTorch framework, aiding developers and writers to suggest refinements and improve policies.
Arun Sai Thunga
Last position:
AI-Backend Developer Intern at Calvergy UA
- Integrated complex AI-based energy system models into the frontend framework, enabling the visualization of insights for 6+ key clients and maximizing energy utilization.
- Maximized energy efficiency and utilization by architecting the seamless data flow between AI models and the user interface for rapid, actionable reporting.
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
Aravind Sasi Nair Purayath
Last position:
AI – Data Specialist at Emirates Islamic Bank
- Architected and deployed LLM based AI agents, RAG pipelines, and vector search solutions for decision support across retail banking department.
- Developed and shipped robust AI pipelines with guardrails, error handling, monitoring, and fallback logic ensuring high reliability outcomes and compliance with data privacy.
- Developed and deployed ML models to identify transactional anomalies, improving fraud detection and risk assessment in high-volume datasets for credit risk modelling.
- Built, evaluated and fine-tuned ML models to generate propensity scores for customers used to drive personalized targeting campaigns for credit cards and personal finance/loan products.
- Developed an NLP pipeline using BERT embeddings and spaCy NER for SMS/email analysis and customer query logs.
- Trained machine learning models using Isolation Forest to classify user behaviour and detect anomalies.
- Extracted, cleaned, enriched and feature engineered datasets from different sources to build feature stores that powered ML model training.
- Led development of dashboards using Power BI, Grafana, and Prometheus to monitor model performances, KPI trends, and marketing metrics.
- Built multi-touch attribution models using logistic regression and time-decay weights to evaluate lead quality.
- Developed scalable ETL pipelines from CRM, T24, SAP, and ERP, supporting millions of monthly transactions.
- Integrated testing and CI/CD workflows for robust data pipeline deployment.
Geraldine Castillo
Last position:
Solution Engineer (Data & ML Integration) at Amadeus Data Processing GmbH
- Designed ML-ready data integration workflows between on-premise systems and cloud platforms (Snowflake, AWS Redshift, Azure), enabling scalable feature engineering and model deployment
- Implemented automated ML pipeline deployment using Python, SQL, and CI/CD tools, reducing model deployment time by 60%
- Developed data transformation logic for master data synchronization across ERP and analytics systems, ensuring data quality for predictive models
- Collaborated with cross-functional teams to translate business requirements into mathematical specifications for ML solutions
Daryoosh Dehestani
Last position:
Data Analyst & MLOps-Engineer at CEWE Group
Set up and operated data-driven analysis and reporting processes in Power BI, Tableau, and SAP
Integrated SAP FICO and Workday data into Power Platform workflows to automate HR reports
Developed predictive ML models for workforce planning and KPI management
Used Azure and GCP (BigQuery, Dataflow) to process large data volumes (Big Data pipelines)
Automated reporting increased analysis efficiency by 40%
Introduced a GCP-based analysis model for employee turnover
Discover over 15,000 top freelancers
Statistics of experts using Azure Machine Learning
Aggregated from the professional profiles of matched freelancers.
Experience
12 years
Position duration
1.9 years
Positions per freelancer
8
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Manufacturing, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
97%
Master's degree or higher
83%
Doctorate
14%
Certifications per freelancer
4
Most common languages
German, English, Hindi
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 Germany 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 Germany 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 projects
Azure Machine Learning is used to train, track, and deploy models in Azure. Companies bring it in for repeatable experiments, model registration, online and batch inference, and controlled rollouts. It is a fit when data science work needs to become a stable production service.
What experts do
Strong Azure ML professionals turn notebooks into maintainable pipelines. They work with compute targets, environments, datasets, and registries, then package models for endpoints or scheduled scoring jobs.
- Training and validation workflows
- Model registry and versioning
- Batch and real-time inference
- Pipeline automation and approvals
Ecosystem fit
Azure Machine Learning connects well with Azure Data Lake, Azure Databricks, Key Vault, and Azure Kubernetes Service. It also pairs with Python, MLflow, and common frameworks such as scikit-learn, PyTorch, and TensorFlow. Teams choose it when security, identity, and cloud operations already live in Azure.
When companies need help
Freelance expertise is useful when an internal team has model ideas but needs production structure. That often means moving from ad hoc notebooks to tracked experiments, setting up deployment patterns, or fixing inconsistent environments. In Germany, companies often want specialists who can work smoothly with English technical docs and cross-border teams.
What good specialists bring
Good Azure ML specialists understand both model work and platform work. They write clean pipelines, handle data access and secrets carefully, and keep deployments observable. They also know when to use Azure ML Studio, when to automate with SDKs, and when another Azure service is the better fit.
Signals to hire
- Models run in notebooks but not in a reliable process
- Training and deployment need version control
- Teams need MLOps, testing, or release discipline
- Azure services already host the data stack
- Inference must scale without manual steps
Frequently asked questions
The facts hiring teams ask for most often when it comes to Azure Machine Learning.
Azure Machine Learning is used to train, manage, and deploy machine learning models on Azure. Companies use it for experiment tracking, model registration, pipeline automation, and endpoints for predictions. It helps move work from notebooks into a repeatable production flow.
Azure ML gives you a more structured path for model lifecycle work than plain scripts. Compared with Databricks, it is often chosen when the priority is model deployment, registry-based governance, and Azure-native operations. Databricks may still be part of the stack for data prep, but Azure ML usually owns the model workflow.
A strong Azure Machine Learning specialist usually knows Python, MLflow, and core Azure services such as Key Vault, Storage, and Azure Kubernetes Service. Skills in CI/CD, Docker, and data engineering also matter because the work often crosses team boundaries. For many projects, solid MLOps habits are as important as model training itself.
A small proof of concept can start with one specialist who knows the service well. Production work usually needs someone who has handled environments, identity, deployment, and monitoring before. If the project touches regulated data or shared Azure estates, the bar is higher because operational mistakes are costly.
Most Azure Machine Learning work can be done remotely because the key tasks live in code, cloud resources, and shared tickets. On-site time can help when stakeholders need workshops, access reviews, or fast alignment with data and platform teams. In Germany, hybrid work is common when multiple internal teams are involved.
Look for clear pipelines, versioned models, and deployments that are easy to repeat. A good Azure ML freelancer explains trade-offs, documents the environment setup, and shows how monitoring and rollback work. You should also expect practical experience with Azure permissions and secure secret handling.
Azure Machine Learning is the current product name, while Azure ML and Azure ML Studio are common ways people refer to it. The older Azure Machine Learning Service name still appears in search and older project docs. Azure AI is broader and includes other services, so it is not the same thing.
A Azure Machine Learning specialist can deliver training pipelines, deployment templates, model registry setup, and scoring jobs. They may also build MLOps checks, automate releases, and clean up experiment tracking so teams can maintain the system later. The best deliverables are practical and easy for an internal team to continue using.
The average hourly rate of freelancers in Germany who have used Azure Machine Learning in their recent projects is 94 €, which corresponds to a daily rate of about 749 € based on an 8-hour working day.
Of the freelancers in Germany who have used Azure Machine Learning in their recent projects, 97% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Germany who have used Azure Machine Learning in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Germany who have used Azure Machine Learning in their recent projects are German (100%), English (100%), and Hindi (26%).
The most common industries among freelancers in Germany who have used Azure Machine Learning in their recent projects are Information Technology (81%), Manufacturing (58%), and Banking and Finance (45%).
The most common business areas among freelancers in Germany who have used Azure Machine Learning in their recent projects are Information Technology (100%), Product Development (87%), and Business Intelligence (71%).
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.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
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Philipp Thomaschewski
FRATCH CEO
