Deep Learning Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Deep 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.
Philipp Grunert
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
Mirza Klimenta
Last position:
Agentic AI for a DeepResearch project at Freelance
- Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
- Used multiple experts (OpenAI models) collaborating during document drafting
- Extracted useful information from the knowledge graph
- Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
- Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
- Deployed initial application as a Streamlit app
Krithika Chand
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 Khamenya
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
Francisco Aguerri
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 Vadali
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.
Clarissa Heinemann
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 Agarwal
Last position:
Business Intelligence Consultant at Large Private Equity Group
- Business intelligence and KPI specification and playbook for 35 European companies.
Tobias Bauernfeind
Last position:
Lead XR Project at BMW Group
- Showcasing the world's first fully immersive AR glasses experience in a moving car at CES 2024.
- Speaker about augmented reality at international conferences (e.g. the AR Ride Concept @ Unite 2024).
- Lead a 12-person interdisciplinary software team developing Android head-unit integrations, navigation & ADAS UI, and embedded software.
- Define technical direction, drive cross-domain architecture and integration, and mentor engineers across Android, UI/UX and embedded stacks.
- Oversee a small fleet of test vehicles for validation, tests, and data collection.
Christian Musewald
Last position:
Independent Management Consultant at CuriousMinds Unternehmensberatung
- Strategy consulting, technical consulting, interim management and training
- Management consulting and development of application solutions through interdisciplinary approaches
- Staff and team development as well as innovation and communication management
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
Roumaissa Troudi
Last position:
Master’s Thesis: AI-Based Analysis of 2D and Exploded View Drawings at Technical University of Munich
- Developed an end-to-end AI pipeline for analyzing 2D exploded-view drawings using computer vision and deep learning models.
- Integrated YOLO-based object detection (Bounding Boxes, Post-Processing, Overlap Handling) for accurate part and callout detection.
- Applied the Segment Anything Model (SAM) for fine-grained segmentation and separation of individual components.
- Implemented OCR and feature extraction modules, and compared Vision Language Models (VLM) and traditional computer vision approaches in terms of accuracy, runtime, and scalability.
Vibhuti Ranjan Singh
Last position:
Global Head of Product | Business Leader at NavVis
Established strategic business units, implemented Objectives and Key Results (ORKs), metrics and management systems, and defined career hierarchies. Led teams across product, program, design, and research domains to optimise organisational efficiency and innovation.
Drove successful market entry for sophisticated digital twin solutions, overseeing a portfolio of DeepTech and SaaS products while seamlessly transitioning to a SaaS business model. Spearheaded competitive market positioning, sales, and distribution strategy within a complex landscape. Held the company’s core business leadership, catalysing a comprehensive organisation-wide restructure to fortify growth.
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, Project Management, Product Development
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
97% (Germany: 88%)
Doctorate
20% (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 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Core use cases
Deep learning is used when rule-based logic is not enough. It powers image recognition, speech systems, text understanding, forecasting, and recommendations. In Munich, companies often bring in specialists for industrial inspection, mobility, media, and product analytics.
Typical stack
- TensorFlow, PyTorch, and Keras for model building
- GPU training, CUDA-aware setup, and experiment tracking
- Python, NumPy, pandas, and notebook workflows
- Model serving with APIs, containers, and cloud deployment
Strong expertise
A strong deep learning professional knows data preparation, architecture choice, training stability, and evaluation. They understand overfitting, class imbalance, transfer learning, and how to tune models without wasting compute. They also document trade-offs clearly for technical and non-technical teams.
When to hire
Companies usually hire freelance help when a model needs to move from prototype to production, or when an internal team needs extra hands on a narrow problem. That can include new data pipelines, retraining workflows, inference optimization, or replacing a weak baseline with a better model.
What deliverables look like
- Trained models and repeatable training code
- Evaluation reports with clear metrics and failure cases
- Inference services for batch or real-time use
- Data preprocessing and feature pipelines
- Deployment notes, handover docs, and monitoring checks
Munich collaboration
Munich teams often work in English across product, data, and engineering groups, while on-site time can help with access to sensitive data or hardware. Freelance specialists should be comfortable with local stakeholders, structured reviews, and practical communication around model results, not just experiments.
Frequently asked questions
Not sure where to start with Deep Learning? These answers cover the essentials.
Deep learning is used for image classification, speech transcription, language understanding, recommendation, forecasting, and anomaly detection. It fits problems where the system must learn patterns from large, messy data rather than follow fixed rules. Strong specialists can also adapt it for domain-specific tasks such as industrial quality checks or search ranking.
Deep learning is a subset of machine learning that uses layered neural networks. It usually needs more data and compute, but it can handle unstructured inputs like images, audio, and text better than many classic methods. For simpler tabular problems, tree-based models may still be easier to train and explain.
A strong deep learning freelancer usually works with PyTorch or TensorFlow, plus Python, NumPy, and data tooling like pandas. For production work, they should also understand GPU training, experiment tracking, containerized deployment, and basic MLOps practices. The exact stack depends on whether the project is research, prototyping, or live inference.
A good deep learning project can start with a problem statement, sample data, and a clear target outcome. The more the team knows about data quality, latency needs, and deployment constraints, the faster the work moves. If those details are unclear, a specialist should first help define the model goal and evaluation plan.
Deep learning can work with smaller datasets if transfer learning, data augmentation, or pre-trained models are a fit. In other cases, simpler models may be more reliable and easier to maintain. A good specialist will compare options instead of forcing a neural network where it does not belong.
Deep learning work is often well suited to remote delivery because data, notebooks, and model code can be shared digitally. In Munich, some teams still prefer on-site sessions for data access, stakeholder reviews, or alignment with internal IT rules. The best setup is usually hybrid, with clear checkpoints and fast feedback.
Look for someone who can explain model choices, data assumptions, and failure modes in plain language. A strong deep learning specialist does not just train a model; they show how it was validated, how it will be monitored, and what happens when data changes. Clean code, reproducible experiments, and honest trade-offs matter more than flashy claims.
Deep learning projects often need solid Python, data engineering basics, cloud deployment, and some MLOps knowledge. For language or vision work, experience with NLP tooling, computer vision methods, and annotation workflows is common. Domain knowledge also matters, especially in regulated or operational settings.
The average hourly rate of freelancers in Munich, Germany who have used Deep Learning in their recent projects is 104 €, which corresponds to a daily rate of about 831 € 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, 97% hold at least a Master's degree, and 20% 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 (27%).
The most common industries among freelancers in Munich, Germany who have used Deep Learning in their recent projects are Information Technology (90%), Automotive (63%), and Education (57%).
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 (93%).
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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Philipp Thomaschewski
FRATCH CEO
