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
Alyosh A.
Last position:
Business Intelligence Consultant at Large Private Equity Group
- Business intelligence and KPI specification and playbook for 35 European companies.
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
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.
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
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.
Stephan B.
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
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.
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.
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.
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).
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
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
91% (Germany: 86%)
Doctorate
19%
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 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 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 29 Oct 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 (91%)
- Automotive (63%)
- Education (53%)
- Professional Services (47%)
- Banking and Finance (41%)
- Healthcare (41%)
- Manufacturing (41%)
- Aerospace and Defense (22%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Deep Learning does
Deep Learning is a branch of machine learning that uses layered neural networks to learn patterns from large and varied datasets. It powers systems that classify images, understand speech, generate text, detect anomalies and make predictions without relying on manually defined rules. Models can improve as they receive suitable training data and feedback.
Products and use cases
Companies use Deep Learning to turn complex, unstructured information into useful results. Typical deliverables include:
- Image recognition, inspection and medical image analysis
- Speech-to-text, conversational interfaces and language processing
- Recommendation, forecasting and fraud detection systems
- Generative AI applications for text, images, audio or code
Ecosystem and tooling
Strong work spans the full model lifecycle. Specialists commonly use Python with PyTorch or TensorFlow, alongside NumPy, pandas and scikit-learn for data preparation and evaluation. They may work with Hugging Face, Jupyter, MLflow, Docker and cloud services for experiment tracking, model serving and reproducible deployments. GPU acceleration and distributed training matter when datasets or models become demanding.
When companies need specialists
Freelance expertise helps when an internal team has valuable data but lacks the capacity to turn it into a reliable product. It is especially useful for selecting an architecture, preparing training data, adapting a foundation model, reducing inference costs or moving a successful prototype into production. In Munich, remote collaboration can work well, while on-site workshops may support teams in automotive, manufacturing, healthcare, finance and media.
Delivery and collaboration
A capable professional clarifies the business objective before choosing a model. They establish data access, labeling rules, privacy constraints and success criteria, then create a repeatable training and evaluation process. They document assumptions, communicate trade-offs and coordinate with product, data, software and infrastructure teams. German and English collaboration may both be relevant for teams operating in Munich.
Signs of strong expertise
Look for evidence that a specialist has delivered models beyond a notebook. Useful signals include:
- Clear validation methods that reflect real operating conditions
- Experience with data quality, bias, drift and explainability
- Production deployment with monitoring, rollback and version control
- Practical decisions about latency, cost, security and maintainability
- Careful handling of sensitive training and user data
Frequently asked questions
Not sure where to start with Deep Learning? These answers cover the essentials.
Deep Learning is used for tasks where rules are difficult to write but examples are available. Common applications include image inspection, document understanding, speech recognition, recommendations, forecasting, anomaly detection and generative AI. The right use case needs enough relevant data and a clear business outcome.
Deep Learning can learn complex representations directly from raw images, audio or text, while traditional machine learning often depends more on engineered features. It can deliver strong results at scale but usually needs more data, computing resources and careful operational controls. A specialist should compare both approaches against accuracy, latency, cost and explainability requirements.
A strong Deep Learning expert often combines Python, statistics, data preparation and software engineering with knowledge of PyTorch or TensorFlow. Experience with cloud infrastructure, GPUs, APIs, MLOps, experiment tracking and model monitoring is valuable for production work. Domain knowledge can also matter greatly in areas such as manufacturing, healthcare or finance.
The right level depends on the assignment, not on a fixed number of years. A prototype may need proven model-building and data skills, while a production system calls for experience with deployment, monitoring, security and failure handling. Ask candidates to explain comparable deliverables, their personal contribution and the trade-offs they made.
Yes, many Deep Learning tasks can be delivered remotely when data access, computing environments and communication routines are well defined. On-site sessions in Munich can help with workshops, sensitive data processes or close collaboration with domain teams. Agree early on access controls, documentation, meeting language and responsibilities for production support.
Assess how the Deep Learning specialist defines the target, separates training from evaluation data and tests performance under realistic conditions. Look for clear reasoning about false positives, bias, drift, latency and operating cost rather than a single impressive score. A quality professional can also explain limitations in plain language.
Deep Learning may be preferable when a company needs a specialized model, strict control over data, predictable behavior or low-latency inference. A managed generative AI service can be faster for broad text or media tasks, but it may limit customization, transparency or deployment options. The decision should consider data sensitivity, integration effort and long-term operating needs.
Before accepting a Deep Learning project, clarify the business objective, available data, labeling process, infrastructure, acceptance criteria and ownership of the resulting model. Confirm whether the work covers research, production integration or ongoing monitoring. Also discuss access to subject-matter experts and how model risks will be reviewed.
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 848 € 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, 91% 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 (28%).
The most common industries among freelancers in Munich, Germany who have used Deep Learning in their recent projects are Information Technology (91%), Automotive (63%), and Education (53%).
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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