Machine Learning Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Machine Learning
Hachem S.
Last position:
Machine Learning & BI Lecturer at EU Business School
- Taught ML, BI, and Big Data to Master's students.
Tech: Python, Power BI, SQL, ML, SnowflakeDB, databricks, MS Fabric, MongoDB, LLM
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
Giuseppe A.
Last position:
Embedded Software Development at Inheco
- AI Integration (LLM & RAG): Designed and built an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data to automatically analyze technical documents and error logs.
- Design & Implementation: Designed a robust RS-232/UART communication interface for an SBC-based embedded device used to control medical shaker systems.
- Architecture & Protocol Design: Built a highly maintainable software structure (OOP, SOLID) and defined resilient, hardware-level communication protocols, including multithreading and advanced error handling.
- Quality Assurance & DevOps: Automated testing with xUnit, ran integration tests directly on the hardware target, and maintained technical documentation to strict medical technology standards via Azure DevOps.
Label: C#, .NET, LLMs, RAG, n8n, RS-232, UART, Multithreading, async/await, xUnit, gRPC/protobuf, Blazor, MudBlazor, EF Core, Visual Studio 2026, Azure DevOps
Sebastian O.
Last position:
Founder & Managing Director at OS-Cons GmbH
- Consulting across two integrated areas: Commercial Strategy (pricing, sales steering, marketing strategy, market expansion, margin management) and Operational Efficiency (process automation, AI integration, workflow design, last-mile automation).
- Development of custom SaaS solutions, explicitly tailored to the specific requirements and processes of each company.
- Delivery of AI training and change management workshops for managing directors and specialist departments, including AI competence training with a certificate of attendance under Art. 4 of the EU AI Act.
Andre K.
Last position:
Nearshore Engagement Manager at EnBW AG
- Building strong awareness within the company around nearshoring
- Engagement Manager in the Nearshore Competence Center
- Responsible for nearshore consulting, partner screening and technical onboarding, designing and implementing cooperation scenarios, change management, stakeholder management, participation in steering committees, and collaboration with IT and business units
- Tools used: Microsoft Office 365, Microsoft Teams, Azure Devops, Microsoft Sharepoint, Conceptboard
- Key results: Establishment of a nearshoring strategy, successful identification and implementation of outsourcing partnerships, delivery of change management and stakeholder management at the highest level
Philipp G.
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Asma K.
Last position:
Data & AI Product Manager – Business & Sales Operations at PUMA GROUP
- Defined the vision, strategy, and roadmap of AI-powered analytics products, ensuring they met the business needs of Sales, Marketing, Finance, and executive teams across Europe.
- Collected business requirements, prioritized AI product features, and led Agile development of forecasting and analytics solutions. Defined product specifications, user stories, and acceptance criteria to ensure successful delivery.
- Collaborated with business stakeholders, Product Owners, data scientists, ML engineers and software engineers to transform AI models into scalable business products and integrate AI insights into operational workflows.
- Designed and implemented Generative AI solutions leveraging Large Language Models (LLMs) to automate reporting and enable natural-language querying of enterprise data, reducing manual effort by approximately 30%.
- Defined product goals and success metrics, tracked product performance and user adoption, and continuously improved the product based on user feedback and business results.
- Established data governance, master data quality and reporting standards across SQL, BigQuery and Power BI environments to ensure reliable, secure and scalable analytics.
Alyosh A.
Last position:
Business Intelligence Consultant at Large Private Equity Group
- Business intelligence and KPI specification and playbook for 35 European companies.
Emanuel F.
Last position:
Interim Architect & Data Taskforce at Freelancer / Project Assignments
- Data Engineering: Design and implementation of scalable data pipelines
- Legacy migrations to Microsoft Fabric (Lakehouse, Dataflows Gen2, Pipelines)
- BO Universe migrations to MS Fabric / Semantic Models / Power BI
- Taskforce for data-driven transformation projects involving Azure Fabric / Oracle / MSSQL
Eduard D.
Last position:
Guest Lecturer at Politehnica University Bucharest
"Reasoning Models: From LLM Foundations to NOC Automation."
Valery K.
Last position:
Sr. Data Scientist & Engineer at Virtual Minds
- Development High-Performance ad distribution via auction
- Holistic (multi-campaign & multi-channel) advertisement placement optimization
- Algorithmic optimization for NP-Hard/NP-e
- Multiple Knapsack Problem with constraints
- Online estimation of parameters in stochastic environments
Tools: Python, R, Kotlin, MILP/SAT/CP Solvers, Pytorch, Pandas, Docker
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).
Sukumar R.
Last position:
AI Consultant
- Architect end-to-end AI/ML solutions, with a strong focus on advanced LLM applications, autonomous agentic AI workflows using MCP toolset, vLM embedding computations, and robust agentic RAG pipelines to drive real-world business impact for clients across diverse sectors.
- Designed intelligent agentic AI workflows to automate knowledge retrieval and reasoning for B2B clients, accelerating business impact.
Ananthraj N.
Last position:
Founder at sprhava
Leading the end-to-end development of Edge AI-powered smart glasses for visually impaired individuals, aligning product vision with user needs and managing a cross-functional team of data scientists, Android developers, AWS engineers, and hardware specialists.
- Defined product roadmap for Edge AI smart glasses and MVP features through user research, stakeholder interviews, and competitive analysis, ensuring accessibility and real-world usability.
- Developed and validated a PoC for AI-driven cancer cell identification in PET/CT scans, collaborating with medical experts to optimize diagnostic accuracy and clinical relevance.
- Built and scaled a multidisciplinary team of 75+ engineers, interns, and designers across Germany and India, driving cross-border collaboration and iterative prototyping.
- Established strategic partnerships with NGOs, healthcare providers, and advocacy groups to embed inclusivity and patient feedback into product design.
- Drove hands-on hardware-software integration using Raspberry Pi and Jetson Nano of AI models. Initiated and nurtured relationships with suppliers, manufacturers, and ecosystem players to build scalable go-to-market plans.
- Owned critical product decisions, from prototype development to funding strategy, applying a data-informed and impact-driven mindset.
- Fostered a learning-focused culture by facilitating brainstorming sessions and continuous feedback loops between engineering and product.
Achievements:
- Public speaker: Auto.Ai 2025 (Berlin), Wearable technologies 2025 (Munich and Bangalore), MEDICA 2024 (Dusseldorf)
- WMF, Bologna, Italy (June 2024): Only AI startup to be selected from Germany as EBV hero to represent sprhava on global platform
- Medica, Germany (2024): Delivered a speech on AI smart glasses in world's largest Healthcare event.
- Wearable Technologies, Bengaluru, India (Dec 2024): I was a speaker presenting sprhava and its product.
- Venturise Global Challenge (GIM 2025, Bengaluru Palace, Karnataka): sprhava was selected as one of the 16 top startups (ESDM) to present on this global platform
- Wearable Technologies Conference 2025 EUROPE, Munich, Germany (May 2025): Delivered a talk on Edge AI at the Europe's biggest wearable tech event.
- InsurNext Köln, Germany (2025): sprhava was honoured with a booth from Cologne administration.
Discover over 15,000 top freelancers
Statistics of experts using Machine Learning
Aggregated from the professional profiles of matched freelancers.
Experience
16 years (Germany: 14 years)
Position duration
2.1 years (Germany: 2.8 years)
Positions per freelancer
10 (Germany: 9)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Automotive, Manufacturing
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
98% (Germany: 97%)
Master's degree or higher
82% (Germany: 76%)
Doctorate
22% (Germany: 18%)
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
99% (Germany: 98%)
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Munich using 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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
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 (84%)
- Automotive (54%)
- Manufacturing (44%)
- Professional Services (43%)
- Banking and Finance (39%)
- Education (37%)
- Healthcare (29%)
- Telecommunication (28%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Machine Learning does
Machine Learning enables software to learn patterns from data and produce predictions, classifications or decisions without relying on fixed rules for every case. Companies use it for demand forecasting, fraud detection, search, recommendations, quality inspection, language processing and image analysis. The work spans experimentation, model development and dependable production systems.
Common applications
Machine Learning appears in products and internal systems across manufacturing, mobility, finance, healthcare, retail and media. Typical deliverables include:
- Predictive maintenance and demand forecasting models
- Recommendation, ranking and personalization services
- Computer vision for inspection, recognition and document processing
- Natural language systems for search, classification and support workflows
- Anomaly detection, risk scoring and fraud prevention
Ecosystem and tooling
Strong specialists choose tools according to the data, model and operating environment. Python is common, with libraries such as scikit-learn, pandas and NumPy for structured data, while TensorFlow and PyTorch support deep learning. Production work may involve SQL, Spark, MLflow, Docker, Kubernetes, cloud services and feature stores.
When companies need specialists
External expertise is useful when a team must turn an unclear business question into a measurable modelling task, improve an existing model or prepare a system for reliable operation. Freelance professionals can review data quality, design experiments, reduce inference costs and connect models to existing applications. They also help establish practical monitoring and retraining processes.
- Data pipelines do not produce reliable training data
- Models perform well in tests but poorly in real use
- A proof of concept needs a path to production
- The team lacks experience with deployment or monitoring
What strong professionals deliver
Good Machine Learning professionals connect technical choices to business outcomes. They define useful evaluation methods, document assumptions and test for leakage, bias, drift and failure cases. They know when a simple statistical model is safer than a complex neural network, and they communicate trade-offs clearly to product, engineering and operational teams.
Collaboration and project fit
Machine Learning work depends on close access to domain knowledge, data owners and the systems that will consume the output. Munich-based companies may prefer on-site workshops for discovery or regulated projects, while remote collaboration works well when data access, documentation and review practices are clear. The right specialist can align modelling, software delivery, security and governance from the start.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Machine Learning.
Machine Learning is used to find patterns in data and turn them into predictions, classifications or automated recommendations. Common applications include forecasting, fraud detection, document processing, recommendation systems, industrial inspection and natural language processing.
Machine Learning learns behaviour from examples, while rule-based software follows conditions written directly by people. It is useful when patterns are difficult to describe manually, but it requires suitable data, careful evaluation and monitoring after release.
A strong Machine Learning specialist usually understands data engineering, statistics, software development and cloud deployment. Depending on the project, useful adjacent skills include SQL, Python, model serving, experiment tracking, MLOps, data privacy and product analytics.
The required depth of experience depends on the data, risk and production setting rather than on the model name. A small proof of concept may need focused modelling skills, while a customer-facing or regulated system calls for experience with data quality, monitoring, security and operational ownership.
Machine Learning work can often be delivered remotely when specialists have secure access to data, clear documentation and dependable review routines. On-site sessions in Munich can still help with discovery, stakeholder alignment and access to physical processes such as manufacturing or inspection.
Assess whether Machine Learning work starts with a clear business objective and a defensible evaluation method. Ask how the specialist handles missing data, leakage, bias, drift, reproducibility, model explanations and performance after deployment.
Deep Learning is often useful for unstructured inputs such as images, audio and text, especially when enough suitable training data and computing capacity are available. Traditional Machine Learning methods can be easier to explain, faster to train and more effective for smaller structured datasets.
Before starting Machine Learning work, clarify the decision the model must support, the available data, success criteria and who owns deployment. Also confirm access controls, labeling responsibilities, expected handover materials and how model quality will be monitored in real use.
The average hourly rate of freelancers in Munich, Germany who have used Machine Learning in their recent projects is 98 €, which corresponds to a daily rate of about 782 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Machine Learning in their recent projects, 98% hold at least a Bachelor's degree, 82% hold at least a Master's degree, and 22% hold a doctorate.
On average, freelancers in Munich, Germany who have used Machine Learning in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Munich, Germany who have used Machine Learning in their recent projects are English (99%), German (96%), and French (26%).
The most common industries among freelancers in Munich, Germany who have used Machine Learning in their recent projects are Information Technology (84%), Automotive (54%), and Manufacturing (44%).
The most common business areas among freelancers in Munich, Germany who have used Machine Learning in their recent projects are Information Technology (93%), Product Development (90%), and Business Intelligence (65%).
Main locations of FRATCH Experts, who have recently used 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.
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Hamburg 
Cologne 
Frankfurt 
Stuttgart 
Dusseldorf 
Dortmund 
Essen 
Bremen 
Dresden 
Hanover 
Nuremberg Request a free demo
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