Machine Learning Experts in Munich
matched in minutes from over 15,000 CVs with the power of AIHire experts who train reliable models, create production-ready data pipelines and apply computer vision or natural language processing to real business problems. FRATCH quickly matches you with vetted, available freelancers whose skills fit your project.
Meet FRATCH Experts in Munich, who have recently used 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.
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
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.
Giuseppe A.
Last position:
Embedded Software Developer at Inheco
- AI Integration (LLM & RAG): Design and build of an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data for the automated analysis of technical documents and error logs.
- Design & Implementation: Design of a robust RS-232/UART communication interface for an SBC-based embedded device to control medical shaker systems.
- Architecture & Protocol Design: Implementation of a highly maintainable software structure (OOP, SOLID) and definition of hardware-close, resilient communication protocols including multithreading and advanced error handling.
- Quality Assurance & DevOps: Test automation using xUnit, integration tests directly on the hardware target, and maintenance of technical documentation according 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
Tezcan D.
Last position:
Solution Architect / Project Manager at German Football Association
- Overall responsibility for the project lifecycle from scope definition to completion
- Close collaboration with platform teams, IT leaders, and external service providers
- Application of SAFe principles and structured sprint work
- Creation of a migration roadmap with clear milestones
- Monitoring of the lifecycle: onboarding, repository migration, replication of permissions, and system tests
- Visualization of the architecture with PlantUML and Gliffy as well as documentation in Confluence
- Regular status reports and running knowledge transfer sessions
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
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).
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.
André H.
Last position:
Linux IT Admin at ReiserST
- Development and maintenance of IT architectures with embedded Linux systems.
- Designing, implementing, and optimizing backend applications and script-based solutions.
- Analyzing and resolving issues, including troubleshooting and user support.
- Developing and implementing security concepts for cloud solutions.
- Administering networks (DHCP, DNS, NTP, VPN).
- Technologies: Linux, PowerShell, Bash, Python, Ansible, Kubernetes, GitLab CI.
- Methods: Kanban.
Thomas H.
Last position:
VP Mid-Market & SaaS Transformation (interim) at WithSecure Corp.
- Led the transition from regional to segment-based GTM structure, rebuilding regional ownership, focus, and accountability, especially around the high-growth MSP partner segment.
- Redesigned and operationalized a cross-functional GTM engine for the partner channel, enabling scalable, data-driven upsell and cross-sell programs.
- Established an agile, data-driven operating rhythm with a unified business planning and execution model including QBRs, forecast reviews, and GTM planning cadences.
- Launched 360° business and partner analytics, providing actionable visibility into performance, partner contribution, churn signals, and growth potential across mid-market segments.
- Restructured and re-aligned RevOps teams, introducing segment-specific roles, ICP-based account segmentation, and a clear focus on expansion plays.
- Authored and implemented the company-wide SaaS Playbook, translating SaaS strategy into standardized GTM practices and launching a modular training curriculum to align regional and functional teams.
Valery K.
Last position:
Sr. Data Scientist & Engineer at Virtual Minds
- Development of 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
Huda I.
Last position:
Senior UX/UI Designer & AI Experience Lead at NTT Data DACH
BMW AG · Mercedes-Benz AG · Munich Re · CARIAD / VW Group
- Leading UX and product direction of PromptM, an enterprise AI prompt management platform at BMW, designing human-agent interaction models, role-based governance flows, and a Playground testing environment for prompt validation and iteration
- Redesigned insurance policy workflows at Munich Re, restructuring form layouts, improving field prioritisation based on user needs, and introducing new design components into the existing product system
- Led end-to-end UX at BMW shopfloor: user flows, wireframes, usability testing, and Figma design system adopted across 5+ agile squads, reducing order processing time by 35%
- UX Coach at Mercedes-Benz, upskilling 50+ team members in user research, usability testing, and agile UX methods; reduced design-to-dev handoff by 30%
- Facilitated 10+ design sprints and discovery workshops; enforced WCAG 2.1 AA accessibility across all deliverables
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
83% (Germany: 77%)
Doctorate
23% (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 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 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.
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 (56%)
- Manufacturing (46%)
- Professional Services (42%)
- Education (37%)
- Banking and Finance (37%)
- Healthcare (29%)
- Retail (29%)
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 use them to make predictions, classifications or recommendations. Companies use it for demand forecasting, fraud detection, search, personalization, quality inspection and process automation. Models can support decisions, generate content or control actions when their limits are clearly defined.
Core project work
Machine Learning specialists turn an idea into a measurable, maintainable system. Their work can include:
- Preparing data, defining target variables and selecting useful features
- Training, validating and tuning supervised or unsupervised models
- Building recommendation, forecasting, vision or language solutions
- Monitoring drift, bias, latency and model performance in production
Ecosystem and tooling
The ecosystem spans Python, SQL and notebooks, with libraries such as scikit-learn, PyTorch, TensorFlow and XGBoost. Strong specialists also work with pandas, Spark, MLflow and experiment tracking, plus cloud services and containerized deployment. The right stack depends on data volume, latency, privacy and operational constraints.
When companies bring in experts
Freelance expertise is useful when internal teams have valuable data but lack a clear path to a production outcome. It can accelerate a new proof of concept, replace a fragile prototype, or improve an existing model and its deployment process. In Munich, collaboration may involve on-site workshops with product, data and operations teams alongside remote delivery.
Signals of strong practice
Look for professionals who connect model quality with business impact instead of optimizing metrics in isolation. They explain assumptions, test against realistic edge cases and document data lineage, reproducibility and failure modes. Experience with security, privacy, responsible AI and stakeholder communication matters as much as knowledge of algorithms.
Delivery and collaboration
A credible engagement starts with a clearly defined decision, usable data and an agreed evaluation method. Specialists should leave behind reproducible training workflows, versioned data and models, deployment documentation and monitoring guidance. Teams working across Germany should also agree early on language, meeting cadence, access controls and ownership of the resulting assets.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Machine Learning.
Machine Learning is used to detect patterns in data and support predictions, recommendations, classifications and automated decisions. Common applications include demand planning, fraud detection, customer segmentation, document processing, image inspection and natural language tools.
Machine Learning learns patterns from examples, while traditional software usually applies rules written directly by a professional. It is useful when the rules are complex or change with the data, but it requires careful training data, evaluation and monitoring.
A strong Machine Learning expert often combines Python, SQL, statistics and data engineering with model deployment skills. Experience with cloud infrastructure, APIs, MLOps, experiment tracking and responsible AI can be important for production work.
The right level for Machine Learning depends on the project’s data quality, risk and production requirements. A focused prototype may need a specialist who can validate the approach, while a regulated or high-volume system needs proven skills in evaluation, deployment, monitoring and governance.
Machine Learning work is often suitable for remote collaboration because data exploration, coding and model reviews are digital. On-site sessions in Munich can still help with discovery, access planning and workshops involving product or operations teams; agree on language and communication routines early.
Before engaging a Machine Learning specialist, define access permissions, data ownership, retention rules and the expected use of personal or sensitive information. The professional should be able to explain how data will be protected, transformed, evaluated and removed when no longer required.
A good Machine Learning deliverable includes a reproducible workflow, a suitable evaluation method and clear documentation of limitations. Check performance on realistic unseen data, inspect important error cases and confirm that deployment, monitoring and rollback steps are practical.
Machine Learning may be a poor fit when a simple rule or established statistical method solves the problem more transparently. It is also risky when data is too sparse, labels are unreliable, the outcome cannot be measured or the business cannot support ongoing monitoring.
The average hourly rate of freelancers in Munich, Germany who have used Machine Learning in their recent projects is 99 €, which corresponds to a daily rate of about 790 € 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, 83% hold at least a Master's degree, and 23% 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 (25%).
The most common industries among freelancers in Munich, Germany who have used Machine Learning in their recent projects are Information Technology (84%), Automotive (56%), and Manufacturing (46%).
The most common business areas among freelancers in Munich, Germany who have used Machine Learning in their recent projects are Information Technology (92%), Product Development (90%), and Business Intelligence (66%).
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.
Berlin 
Hamburg 
Cologne 
Frankfurt 
Stuttgart 
Dusseldorf 
Dortmund 
Essen 
Bremen 
Dresden 
Hanover 
Nuremberg Request a free demo
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FRATCH CEO
