Deep Learning Experts
to turn complex data into reliable AI with fast, precise matchingHire experts who build and optimize image recognition, language models, recommendation systems and forecasting pipelines with Python, PyTorch or TensorFlow. FRATCH matches you quickly with vetted, available freelancers whose skills fit your project.
Meet FRATCH Experts who have recently used Deep Learning
Gabin Maxime N.
Last position:
Multi-Agent R&D Pipeline (3 Custom Agents) at Independent Project
Claude Code subagents, MCP, Pydantic V2, pytest, bandit
Designed and shipped 3 specialized agents that hand work down a line: a research agent writes a cited implementation spec, a coding agent builds the modular code and its tests, a review agent ranks findings by severity and applies the fixes. Each handoff is a structured document, so no stage depends on another agent's context window.
Connected the research agent to an academic-research MCP server (Semantic Scholar, ArXiv, Hugging Face Hub, citation snowballing) so every reference traces to a tool result rather than the model. Gated commits behind ruff, mypy, pytest and bandit, required human sign-off before installs and commits, and persisted session state on disk so long runs survive a context reset.
Peter S.
Last position:
Senior ML Engineer & AI Researcher at Anonymous Client
Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing
- Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
- Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
- Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.
Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision
Peter S.
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 S.
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
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
Karin A.
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.
Sven W.
Last position:
Simulation of Photometric-Stereo Setups at ID Engineering
- Role: Simulation Engineer
- Environment: Mechanical Engineering / Visual Inspection
- Goals & Implementation: Simulation of photometric-stereo setups to determine the best positions for cameras and light sources for each specific part.
- Business Value: Enabled a low-cost and scalable solution for determining part-specific hardware setups.
- Tech Stack: Python, Blender
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.
Shanna T.
Last position:
Freelance Data Scientist & AI Developer at tellaev.de
- Portfolio development & customer acquisition
- Portfolio development (RAG, NLP fine-tuning, process automation with n8n) and active customer acquisition
- Positioning: GDPR-compliant, locally hosted AI solutions for SMEs
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
Bora D.
Last position:
Software Architect at DZ HYP AG
- Lead architect for an enterprise loan digitisation programme, coordinating 12 engineers across five workstreams and serving as final technical authority on system design.
- Cut critical application response times by 68% (display 17.3s → 5.6s; modification 11.8s → 5.0s) through targeted caching, OData query optimisation and lazy-loading refactoring; further optimisation in progress.
- Own production error triage, prioritisation and resolution across a multi-application portfolio supporting live lending operations.
- Design and implement SAP Fiori applications on SAP UI5, TypeScript and RAP, owning delivery from architecture and code through rollout and production support.
- Established C4 architecture documentation and decision records for the full programme, enabling faster onboarding and consistent cross-workstream design governance.
- Co-managed S/4HANA release cycle alongside primary responsibilities, coordinating directly with SAP support to resolve critical system issues across the portfolio.
Samuel K.
Last position:
Founder & Agentic AI Engineer at Agentakt LLC
Independent engineering practice focused on custom AI systems, production delivery, and fractional technical leadership.
Selected client engagement: Scalutions
Role: Serve as fractional CTO and hands-on technical lead, responsible for the architecture and agentic infrastructure behind its managed B2B outbound operation.
Product: Designed and built OutboundLoop, an agentic SDR operating system for research, qualification, personalized outreach, campaign management, human approvals, measurement, and continuous improvement.
Scope: Own the full system lifecycle—from business processes and agent behavior to context design, model routing, integrations, evaluation, telemetry, reliability, cost control, and production operations.
Anjaneya M.
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 B.
Last position:
Senior AI Engineer at Just Add AI GmbH
- Automatic detection of content on various documents
- Recommendation Engine
- Dynamic Pricing
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 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
87%
Doctorate
18%
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
99%
Based on our profile pool as of 26 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology 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 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 26 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 (84%)
- Education (56%)
- Automotive (42%)
- Manufacturing (40%)
- Healthcare (35%)
- Banking and Finance (33%)
- Professional Services (32%)
- Retail (21%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What It Does
Deep Learning uses multilayered neural networks to learn patterns from large and complex datasets. It powers systems that interpret images, text, audio, video and sensor signals. Unlike rule-based software, deep neural networks can discover useful representations during training and apply them to new data.
Common Applications
Companies use this technology when conventional machine learning cannot capture the structure of their data.
- Image classification, object detection and visual inspection
- Natural language processing, search and conversational systems
- Speech recognition, transcription and audio analysis
- Recommendations, anomaly detection and demand forecasting
Core Ecosystem
Python is the main working language, supported by libraries such as NumPy, pandas and scikit-learn. PyTorch and TensorFlow provide model-building and training capabilities, while Hugging Face supports modern language and vision models. Specialists also work with CUDA, GPUs, MLflow, Docker and cloud infrastructure for repeatable experiments and deployment.
Project Delivery
Freelance expertise is useful when a company needs to move from a promising prototype to a dependable production system. Professionals prepare and label data, select model architectures, design training pipelines and expose models through APIs. They also establish reproducible experiments, monitor quality and plan retraining as data changes.
Signs You Need Help
Bring in a specialist when model work is blocked by data quality, slow training or unclear evaluation criteria.
- Existing models perform well in testing but fail with real-world inputs
- Training costs, GPU use or experiment tracking are difficult to control
- A proof of concept needs a scalable inference service
- Privacy, explainability or bias requirements affect model design
Strong Specialists
Strong professionals connect mathematical foundations with practical software delivery. They understand optimization, embeddings, attention mechanisms, convolutional and recurrent architectures, and transfer learning. They define meaningful validation sets, check for leakage and bias, compare results against a simple baseline, and explain trade-offs to product and engineering teams. Quality also means clear documentation, maintainable code and monitoring after release.
Frequently asked questions
Curious about Deep Learning? Here are the answers that come up again and again.
Deep Learning is used for tasks involving complex data such as images, language, speech, video and sensor streams. Typical systems include visual inspection, document processing, recommendations, forecasting, search and conversational interfaces.
Deep Learning uses layered neural networks that can learn representations directly from large datasets, often reducing the need for manually designed features. Traditional machine learning can be more efficient and easier to explain when data is structured, limited or governed by clear rules.
A strong Deep Learning specialist usually combines Python, data preparation, statistics and software engineering with model training. Experience with PyTorch or TensorFlow, cloud or GPU infrastructure, APIs, experiment tracking and responsible AI practices is also valuable.
The right Deep Learning experience depends on the project scope, data maturity and operational risk. A prototype may need strong modeling and experimentation skills, while a production system also requires deployment, monitoring, testing, security and a clear plan for retraining.
Deep Learning projects are often suitable for remote collaboration because data, code and experiments can be managed in shared environments. On-site work may help when specialists need direct access to proprietary equipment, sensitive datasets or teams responsible for physical processes.
Assess whether the Deep Learning freelancer can explain data assumptions, baselines, validation design and failure cases, not just model architecture. Ask how they would detect leakage, bias and drift, and how they would make the resulting system reliable after deployment.
Choose Deep Learning when the problem involves unstructured data or complex patterns that simpler approaches cannot represent well. For small datasets, strict interpretability needs or straightforward tabular problems, a simpler model may be faster to validate and easier to maintain.
Deep Learning commonly refers to neural networks with multiple processing layers, often called deep neural networks or DNNs. The terms describe a broad family of methods rather than one product, and the architecture should be selected according to the data and business objective.
The average hourly rate of freelancers who have used Deep Learning in their recent projects is 86 €, which corresponds to a daily rate of about 690 € based on an 8-hour working day.
Of the freelancers who have used Deep Learning in their recent projects, 98% hold at least a Bachelor's degree, 87% hold at least a Master's degree, and 18% hold a doctorate.
On average, freelancers who have used Deep Learning in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers who have used Deep Learning in their recent projects are English (99%), German (98%), and French (21%).
The most common industries among freelancers who have used Deep Learning in their recent projects are Information Technology (84%), Education (56%), and Automotive (42%).
The most common business areas among freelancers who have used Deep Learning in their recent projects are Information Technology (93%), 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 all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Berlin 
Hamburg 
Munich 
Nuremberg In Austria our freelancers and interim experts support companies from Vienna to Graz — on-site where your project needs them, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Vienna Graz Across Switzerland our specialists are active in Zurich, Geneva, Basel and Bern — working on-site or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Zurich Geneva Basel Bern Countries:
Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
