Deep Learning Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Deep 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
Felix S.
Last position:
App Developer at XIXUM-Modeler
- Developing a model-based AI where natural language is interpreted as formal relations.
- Natural language terms are not considered rigid but fluid and can be negotiated in a context so meaning resolves by iteratively specifying.
- Develops all kinds of model solutions.
- Backed by natural language and data annotation.
- Requirements to code and other solutions.
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
Krithika C.
Last position:
Professional Reorientation at Von Rundstedt
- Engaged in a structured career development program while strengthening German language proficiency (B1 level) and evaluating opportunities in ADAS/AD systems and requirements engineering.
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
Christian M.
Last position:
Self-employed business consultant at CuriousMinds Unternehmensberatung
- Strategic consulting, technical consulting, interim management, and training
- Management consulting and development of application solutions through interdisciplinary solution approaches
- Employee and team development as well as innovation and communication management
Stephan B.
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Narges D.
Last position:
Research Assistant at Hochschule München
Introduced an integrated approach for structural damage detection across concrete, steel, and glass using advanced technologies such as LiDAR and thermal imaging. Highlighted cross-material interactions to enhance diagnostics and enable predictive maintenance.
Developed an NLP-based medical note simplifier that transforms complex clinical instructions into plain, child-level English. Applied prompt engineering with Flan-T5 transformer models to extract patient-relevant actions and rephrase them into clear to-do items. Built dual Flask and Tornado backends with a printable web interface.
Francisco A.
Last position:
Senior Digital Transformation, AI Strategy Lead & Product / Change Consultant at Francisco Asensio Aguerri Consulting
- Specialist in Digitalisation and PLM Strategies: Extensive experience in leading complex digital transformation initiatives and implementing Product Lifecycle Management (PLM) platforms to enhance efficiency and streamline processes. Focus on developing future-proof data models and interfaces to ensure successful implementation and continuous business improvement.
- Expert in Change, Quality, and Release Management: Design and implementation of end-to-end release and change management processes for the automotive industry, with a strong emphasis on cross-functional collaboration and the use of cloud-based tools such as 3DEXPERIENCE (3DX).
- Consulting in AI-Driven Strategies: In-depth knowledge of artificial intelligence, backed by certifications and hands-on experience in implementing AI technologies to drive innovation and business optimisation.
- Project Leadership in the Automotive Sector: Successful leadership and coordination of vehicle dynamics and quality projects for premium vehicles. Extensive expertise in budget and quality management as well as overseeing technical modifications throughout the development process.
Raghu Ram V.
Last position:
Telco Customer Churn Prediction – End-to-End ML Pipeline at Self-Initiated Project
- Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
- Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
- Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
- Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
- Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
- Exported reusable pipelines and trained models with joblib for deployment.
Anton K.
Last position:
Head of Overall Technical Integration NSC / Hadoop Cloud Development at IABG
Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).
Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.
Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management lifecycle.
Kubernetes, OpenStack and Hadoop are used as the foundation.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.
Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.
Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, DB.
Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
Kaan K.
Last position:
Computer Vision Engineer at Axulus Reply GmbH
- Computer vision engineer responsible for development of industrial vision solutions, beginning as a working student and transitioning to a full-time role in May 2025.
- Designed and implemented vehicle detection and counting models; integrated the pipeline into a cloud-deployed system (Azure) that delivers live analytics dashboards.
- Building an offline print quality assurance system that scans corrugated-board prints on production lines to detect and classify defects such as splashes, impurities and colour deviations, deploying the solution on Jetson edge devices.
- Collaborated with cross-functional teams while focusing on computer vision components, containerization, and deployment.
Clarissa H.
Last position:
AI Trainer at Komdis GmbH
- Led comprehensive AI workshops for professionals, focusing on AI-driven process automation.
- Tech Stack: n8n, Make, LLMs (OpenAI, Anthropic), Prompt Engineering, Process Mapping Tools.
Alyosh A.
Last position:
Business Intelligence Consultant at Large Private Equity Group
- Business intelligence and KPI specification and playbook for 35 European companies.
Discover over 15,000 top freelancers
Statistics of experts using Deep Learning
Aggregated from the professional profiles of matched freelancers.
Experience
15 years (Germany: 13 years)
Position duration
2.1 years (Germany: 2 years)
Positions per freelancer
10 (Germany: 8)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Education
Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
94% (Germany: 87%)
Doctorate
19% (Germany: 18%)
Certifications per freelancer
2
Most common languages
English, German, Spanish
Speak two or more languages
100% (Germany: 99%)
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 Deep 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.
Deep 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 (90%)
- Automotive (65%)
- Education (55%)
- Professional Services (45%)
- Manufacturing (42%)
- Banking and Finance (39%)
- Healthcare (39%)
- Aerospace and Defense (23%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Deep Learning Does
Deep Learning uses layered neural networks to learn patterns from large and complex datasets. It powers systems that classify images, understand language, detect anomalies, generate content, and make predictions. Models can improve as they process representative training data and feedback.
Systems and Use Cases
Companies use Deep Learning where rules alone cannot capture the required complexity:
- Image inspection, medical imaging support, and video analysis
- Speech recognition, translation, and conversational interfaces
- Recommendation, forecasting, and fraud or anomaly detection
- Generative text, image, audio, and multimodal applications
Frameworks and Tooling
Strong specialists work across Python and major frameworks such as PyTorch, TensorFlow, and Keras. They use notebooks and experiment tracking for research, then connect training workflows with data pipelines, model registries, GPUs, containers, and cloud services. Knowledge of CUDA, ONNX, Hugging Face, and vector databases can matter for specific products.
When Companies Need Help
Freelance expertise is valuable when a team must move from a proof of concept to a tested model, production service, or repeatable training process. It can also close gaps in data preparation, model evaluation, GPU infrastructure, inference speed, or responsible deployment. In Munich, specialists may support local industrial, automotive, healthcare, or financial projects while collaborating remotely with distributed teams.
What Strong Specialists Deliver
A capable professional starts with a measurable problem rather than choosing a fashionable model. They establish data quality checks, create defensible evaluation splits, compare suitable architectures, and document assumptions. They also consider explainability, privacy, bias, monitoring, retraining, and the cost of serving predictions after launch.
Working Together Successfully
The best engagement defines the target outcome, available data, technical environment, and decision owner early. Remote collaboration works well when repositories, datasets, experiment logs, and acceptance criteria are organized; on-site workshops can help with domain knowledge and stakeholder alignment in Munich. German or English communication should match the project team and its users.
Frequently asked questions
Not sure where to start with Deep Learning? These answers cover the essentials.
Deep Learning is used for image and video analysis, speech processing, language tools, recommendation systems, forecasting, anomaly detection, and generative applications. It is most useful when the problem involves complex patterns that are difficult to describe with fixed rules.
Deep Learning can learn representations directly from raw or lightly processed data, while many traditional machine learning methods depend more on carefully designed features. It often needs more data and computing capacity, so methods such as gradient-boosted trees may be a better fit for smaller, structured datasets.
A strong Deep Learning specialist may also bring skills in data engineering, statistics, Python, cloud infrastructure, MLOps, GPU optimization, and software testing. For language projects, experience with transformers and retrieval systems can matter; for visual systems, image processing and domain-specific labeling are often important.
The right level of Deep Learning experience depends on the task, data quality, risk, and production requirements. A focused prototype may need a specialist who can select and evaluate models, while a regulated or high-volume system also requires experience with monitoring, security, reproducibility, and model lifecycle management.
Yes, Deep Learning work can usually be delivered remotely when data access, computing environments, and documentation are available. On-site sessions in Munich can still help when specialists need direct access to physical equipment, confidential domain knowledge, or close workshops with business and technical teams.
Assess whether the Deep Learning professional explains data limitations, baseline models, evaluation design, and failure cases clearly. Review production outcomes, not only demo accuracy, and ask how they handle reproducibility, drift, monitoring, privacy, and the operational cost of inference.
A Deep Learning project may benefit from transformers when it involves language, long sequences, vision, audio, or multimodal inputs. The choice should follow the data, latency, available hardware, and maintenance needs; a smaller specialized model can be preferable to a large general model.
Before starting a Deep Learning engagement, clarify the business decision the model will support, the ownership and access rights for training data, the target metrics, and the deployment environment. Also confirm who approves labels, who maintains the service, and what evidence is needed before the system can be used in production.
The average hourly rate of freelancers in Munich, Germany who have used Deep Learning in their recent projects is 106 €, which corresponds to a daily rate of about 851 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Deep Learning in their recent projects, 100% hold at least a Bachelor's degree, 94% hold at least a Master's degree, and 19% hold a doctorate.
On average, freelancers in Munich, Germany who have used Deep Learning in their recent projects have 15 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 Deep Learning in their recent projects are English (100%), German (97%), and Spanish (29%).
The most common industries among freelancers in Munich, Germany who have used Deep Learning in their recent projects are Information Technology (90%), Automotive (65%), and Education (55%).
The most common business areas among freelancers in Munich, Germany who have used Deep Learning in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (94%).
Main locations of FRATCH Experts, who have recently used Deep 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 
Nuremberg Request a free demo
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