Deep Learning Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Deep Learning
Peter Sachs-Witzel
Last position:
Project Manager for SAP GTS Trade Services, edition for SAP 4/HANA at ETENGO AG
- Creation of detailed concepts for all relevant GTS modules (compliance, pricing, customs clearance, Intrastat, preferences)
- Planning and conducting workshops, including scheduling and documentation
- Development and consulting of the organisational structure and its master data
- Setup and customisation of compliance functions: sanctions list review, embargo, legal control, US re-export
- Uploading and maintaining sanction and commodity lists
- Implementation and validation of integration tests
- Setup and customisation of the modules: pricing (including uploading of customs tariff numbers and STAWN data), customs clearance (including application for test operation with customs), Intrastat and preferences (including preference calculation and LLEs)
- Development and presentation of prototypes
- Derivation of insights for the final concept
Fadi Shoaa
Last position:
Development of a production-ready Enterprise Document AI & Recommendation Platform at Freelancer
- Development of a production-ready Enterprise AI solution for the automated processing of invoices and business documents
- Integration of Azure AI Document Intelligence and LLM technologies into existing business processes
- Development of robust REST APIs for automated document processing and system integration
- Extraction, validation, and storage of structured invoice data in Azure SQL as a base for analytics and machine learning models
- Development of an AI-based recommendation engine with machine learning and deep learning to generate personalized product recommendations based on historical purchase data
- Implementation of logging, monitoring, error handling, and validation mechanisms for stable production use
- Collaboration with business teams to define business rules and integrate the solution into existing enterprise processes
Technologies: Python, Azure AI Document Intelligence, Azure OpenAI, Azure SQL Database, REST APIs, Machine Learning, Deep Learning, OCR, Pandas, JSON, Workflow Automation
Karin Albiez
Last position:
AI Benchmark Engineer | Native language specialist German at Lilt
- Task Engineering: Evaluating Coding Agents.
- Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
- Prompting & Translation: finding failure points where AI does not work, in German.
- Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
- Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
- Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
- Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.
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
Anjaneya Marimireddygari
Last position:
Machine Learning Engineer Intern at Slash Mark
- Built and fine-tuned CNN and RNN architectures using transfer learning for real-world classification tasks — core deep learning skills applicable to BMW's multimodal LLM and GenAI vehicle function development.
- Implemented Dropout, Batch Normalisation, and Early Stopping across deep learning experiments; evaluated rigorously using precision, recall, F1-score, and confusion matrices for production-grade reliability.
- Developed an AI-powered attendance management system using LBPH facial recognition, deployed via Flask web interface with real-time SMS notifications — demonstrating end-to-end AI product delivery for real users.
- Collaborated across cross-functional teams to deliver scalable, documented ML pipelines designed for reproducibility — matching BMW's interdisciplinary team and research environment.
- Integrated AI tooling directly into the development workflow from design through to testing, maintaining high velocity without compromising correctness.
Danny-Michael Busch
Last position:
Senior AI Engineer at Just Add AI GmbH
- Automatic detection of content on various documents
- Recommendation Engine
- Dynamic Pricing
Benjamin Matschke
Last position:
Founder, system architect, and main developer at Institute for Artificial Study (IAS)
- Expert-supervised AI systems for scientific reasoning, model evaluation, and research workflows.
- Built the IAS Problem Solver, an orchestrated system for difficult mathematical reasoning; it achieved 84% in one submitted answer set on the Leipzig mathematics benchmark.
- Built a resumable state-machine pipeline for research-grade mathematics benchmark generation: source selection, LLM-agent-based phenomenon discovery, task synthesis, gold-answer and certificate generation and validation, probing, repair, human feedback, and quality gates, targeting tasks that are difficult, natural, verifiable, and cost-effective.
- Current work extends this into budget-aware AI research workflows for real scientific problems with expert review.
Tech stack: Python, OpenAI/OpenRouter-compatible APIs, embeddings, RAG, SQLite.
Lino Giefer
Last position:
Senior Data Scientist at VinFast Germany GmbH
- Led strategic software development of fusion algorithms for precise object tracking, trajectory prediction, and environment modeling based on multimodal sensor data (e.g., camera, LiDAR, radar, GNSS, IMU)
- Developed and implemented navigation algorithms for autonomous vehicles, including path planning, obstacle avoidance, and sensor fusion of visual, inertial, and distance-based sensor sources
- Automated extraction and training processes with CI/CD
- Developed and optimized data pipelines and processes in Microsoft Azure using Apache Spark, Databricks, and PySpark
- Developed and optimized embedded software for automotive control units
- Designed latency-critical software for real-time control in robotic systems with RTOS (freeRTOS, SAFERTOS)
- Used the Vector toolchain (CANdela, DaVinci, CANoe) for configuration and diagnostics
- Optimized existing data pipelines and processes (ETL, data warehouse, SQL)
- Developed and trained machine learning models using PyTorch
- Created deep-learning-based object detection and visual SLAM algorithms, trained on combined data from camera, LiDAR, and IMU sensors
- Implemented computer vision algorithms for object detection and classification in robotic systems using OpenCV and YOLO, utilizing synchronized image and depth data
- Implemented behavior-based control systems for autonomous robots using ROS2 Behavior Trees
- Performed testing, release, and integration of sensor fusion algorithms into automotive production programs
- Ensured adherence to proper software development processes and safety standards to guarantee high data quality (MISRA, ISO 26262, ASPICE)
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
Shanna Tellaev
Last position:
Problem Resolution Manager at CARIAD SE (VW AG), formerly CARMEQ GmbH (VW AG)
- Automotive SPICE®: all assessments fully achieved
- Agile transformation: V-model → SAFe successfully implemented
- Series release: on-time, quality-assured software delivery for key Volkswagen Group models (including ECE homologation)
- Stakeholder management: internal & external
- Process optimization: implemented a continuous improvement process (CIP) with a tracking system
Sanchit Bhavsar
Last position:
Freelancer at S2S Dynamics UG
- Implementing cross-industry applications with LLMs
- Developing cloud infrastructure for clients
- Implemented end-to-end data pipeline to deploy models in real time
- Managed overall IT system administration and desktop support
Rutger Boels
Last position:
Partner & Managing Director at AI.IMPACT
- Building an AI & Data Consultancy Practice with the goal of helping European companies adopt Artificial Intelligence and modern data platforms
- End-to-end further development of a production system using modified coding agents (OpenCode). Tech stack: Kubernetes, Argo, Keycloak, Typescript, Grafana, GitOps, DevOps, Playwright
- Internal research project on the use of coding agents in the field of mathematical logic for creating formal models. Use of Cursor IDE and Codex, Codex CLI. Architecture design, quality control and refactoring, as well as writing code and tests. Repository (open source) available pre-launch
- Research on the role of mathematical logic as a formal language that connects IT and AI with business processes
- Project lead for collecting and deploying parking recommendations for rail vehicles with significant savings potential based on real-time data in a mobility and transport company
- Project lead for collecting and distributing process measurement points for real-time control in a mobility and transport company
- Deputy application owner for an app used for communication in the dispatching and provision of rail vehicles
Cris Lovell-Smith
Last position:
Head of AI at Harvest Hub
- Leading AI development for aquaculture startup, optimising shellfish visual assessments with machine learning and computer vision.
- Development and systematic evaluation of ML/CV algorithms for shellfish condition and morphometrics, using Python, Pytorch and MLFlow.
- Analysis of model performance, including identification of failure modes and edge cases in production deployments.
- Design of annotation strategies and refinement of labelled datasets for computer vision tasks.
- Detailed analysis of system performance and communication of findings through publication-quality technical reports to investors and fellow R&D staff.
- Responsible for delivery of technical roadmap.
David Onaiyekan
Last position:
Research Intern at Pattern Recognition Lab
- Spearheaded the integration of a custom Transformer-based encoder into the AFFGANwriting pipeline, replacing the legacy VGG19 architecture to capture richer, high-fidelity writer-style representations.
- Boosted user-study pick-rates by 40%, demonstrating a significant leap in the perceptual quality and realism of the generated handwriting compared to the baseline model.
- Enhanced OCR performance by 20% by implementing a teacher-student framework that leveraged a TrOCR benchmark model for auxiliary training alignment
Discover over 15,000 top freelancers
Statistics of experts using Deep Learning
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
2.1 years
Positions per freelancer
8
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Information Technology, Education, Automotive
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
98%
Master's degree or higher
88%
Doctorate
18%
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
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 Germany 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 Germany 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
What it covers
Deep learning is a machine learning approach built on neural networks with many layers. It is used when systems need to learn patterns from large and messy data, such as images, text, audio, video, or sensor streams. Strong specialists turn it into working models, not just notebooks.
Typical work
- Image classification, detection, and segmentation
- Text search, extraction, and language models
- Forecasting and anomaly detection from time series
- Speech, recommendation, and ranking systems
- Model training, evaluation, and deployment
Tools and stack
Most projects revolve around PyTorch, TensorFlow, Keras, and CUDA-based GPU workflows. Experts also work with data pipelines, experiment tracking, model serving, and feature stores. Good specialists know how to move from research code to reliable production systems.
When companies hire
Teams bring in freelance deep learning specialists when an internal group needs extra hands, faster delivery, or a hard-to-fill skill set. This is common in Germany for industrial inspection, mobility, commerce, health, and media projects. Remote work fits model development well, while on-site sessions help with data access and stakeholder alignment.
What strong specialists do
Strong professionals understand data quality, model selection, overfitting, latency, and deployment trade-offs. They can explain why a CNN, transformer, or autoencoder fits a task and when it does not. They write clean training code, document decisions, and hand over models that can be maintained.
How to brief the work
A clear brief should cover the data source, target output, success criteria, and the environment where the model must run. If you already use PyTorch, TensorFlow, or Keras, mention that early. For Germany-based teams, it also helps to state whether the expert should work in English, German, or both.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Deep Learning.
Deep Learning is used for tasks where patterns are too complex for simple rules, such as image recognition, document understanding, speech processing, recommendation, and forecasting. It is a fit when the team needs a model that learns from examples and can improve with better data. In practice, the deliverable is often a trained model plus the code and workflow around it.
Deep Learning usually handles unstructured data better than classical methods, especially images, audio, and text. Classical machine learning can be easier to train, explain, and run on smaller data sets. A good specialist knows when a simpler model is the better choice and when neural networks are worth the added complexity.
A strong Deep Learning specialist should know data preparation, model design, evaluation, and deployment basics. Adjacent skills often include Python, PyTorch or TensorFlow, SQL, cloud tooling, and GPU workflows. For production work, experience with MLOps, testing, and monitoring is a major plus.
A Deep Learning project can start with a focused specialist if the use case is narrow and the data is already available. More complex work, such as production vision systems or language pipelines, benefits from someone who has shipped models before and can handle edge cases. The key is matching the scope to the task, not just the label.
Yes, Deep Learning work is often remote because training, review, and iteration can be done online. Germany-based teams may still want some on-site time for data access, workshop sessions, or close work with domain experts. The best setup depends on how sensitive the data is and how much direct collaboration is needed.
Ask which data the Deep Learning specialist needs, what model family they recommend, and how they will measure success. You should also ask how they handle training, validation, deployment, and future maintenance. Clear answers here show whether they think beyond the prototype.
The right choice for Deep Learning depends on your team and target system. PyTorch is common for research-heavy work and flexible iteration, while TensorFlow is often chosen for existing production stacks and certain deployment paths. A strong specialist can work with either and explain the trade-offs clearly.
Look for evidence that the Deep Learning freelancer can explain data choices, evaluation metrics, and deployment limits in plain language. Strong signs include clean code, reproducible training, and clear reasoning about failure modes. Ask for examples of shipped work, not just model ideas.
The average hourly rate of freelancers in Germany who have used Deep Learning in their recent projects is 86 €, which corresponds to a daily rate of about 691 € based on an 8-hour working day.
Of the freelancers in Germany who have used Deep Learning in their recent projects, 98% hold at least a Bachelor's degree, 88% hold at least a Master's degree, and 18% hold a doctorate.
On average, freelancers in Germany who have used Deep Learning in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Germany who have used Deep Learning in their recent projects are German (98%), English (98%), and French (21%).
The most common industries among freelancers in Germany who have used Deep Learning in their recent projects are Information Technology (83%), Education (56%), and Automotive (41%).
The most common business areas among freelancers in Germany who have used Deep Learning in their recent projects are Information Technology (92%), Research and Development (86%), and Product Development (84%).
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 
Munich 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
